Proxy computing system, computing apparatus, capability providing apparatus, proxy computing method, capability providing method, program, and recording medium

ABSTRACT

A computing apparatus outputs τ 1  and τ 2  corresponding to a ciphertext x, a capability providing apparatus uses τ 1  to correctly compute f(τ 1 ) with a probability greater than a certain probability and sets the result of the computation as z 1 , uses τ 2  to correctly compute f(τ 2 ) with a probability greater than a certain probability and sets the result of the computation as z 2 , the computing apparatus generates a computation result u=f(x) b x 1  from z 1 , generates a computation result v=f(x) a x 2  from z 2 , and outputs u b′ v a′  if the computation results u and v satisfy a particular relation, where G and H are groups, f(x) is a function for obtaining an element of the group G for xϵH, X 1  and X 2  are random variables having values in the group G, x 1  is a realization of the random variable X 1 , and x 2  is a realization of the random variable X 2 .

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a divisional application of and claims the benefit of priority under 35 U.S.C. § 120 to U.S. application Ser. No. 13/881,111, filed Jul. 9, 2013, the entire contents of which is hereby incorporated herein by reference and which is a national stage of International Application No. PCT/JP2011/074546, filed Oct. 25, 2011, which is based upon and claims the benefit of priority under 35 U.S.C. § 119 to prior Japanese Patent Applications No. 2010-239342, filed Oct. 26, 2010; No. 2011-005899, filed Jan. 14, 2011; No. 2011-077779, filed Mar. 31, 2011; and No. 2011-088002, filed Apr. 12, 2011, the entire contents of each of which is hereby incorporated herein by reference.

TECHNICAL FIELD

The present invention relates to a technique to perform computation using a result of computation performed on another apparatus.

BACKGROUND ART

Decryption of a ciphertext encrypted using an encryption scheme such as public key cryptography or common key cryptography requires a particular decryption key (see Non-patent literature 1, for example). In one of existing methods for a first apparatus that does not have a decryption key to obtain a result of decryption of a ciphertext, a second apparatus that has a decryption key provides the decryption key to the first apparatus and the first apparatus uses the decryption key to decrypt the ciphertext. In another existing method for the first apparatus to obtain a result of decryption of a ciphertext, the first apparatus provides the ciphertext to the second apparatus and the second apparatus decrypts the ciphertext and provides the result of decryption to the first apparatus.

PRIOR ART LITERATURE Non-Patent Literature

-   Non-patent literature 1: Taher Elgamal, A Public-Key Cryptosystem     and a Signature Scheme Based on Discrete Logarithms, IEEE     Transactions on Information Theory, v. IT-31, n. 4, 1985, pp.     469-472 or CRYPTO 84, pp. 10-18, Springer-Verlag

SUMMARY OF THE INVENTION Problems to be Solved by the Invention

However, in the method in which the second apparatus provides a decryption key to the first apparatus, the decryption key needs to be taken out from the second apparatus to the outside, which poses security concerns. On the other hand, in the method in which the first apparatus provides a ciphertext to the second apparatus and the second apparatus decrypts the ciphertext, the first apparatus cannot verify the validity of decryption performed by the second apparatus. These problems can be generalized to other processing besides decryption. That is, there was not a technique for the second apparatus to provide only a computing capability to the first apparatus without leaking secrete information so that the first apparatus uses the computing capability to correctly perform computations.

Means to Solve the Problems

According to the present invention, a computing apparatus outputs first input information τ₁ and second input information τ₂ that are elements of a group H and correspond to a ciphertext x, a capability providing apparatus uses the first input information τ₁ to correctly compute f(τ₁) with a probability greater than a certain probability to provide the result of the computation as first output information z₁ and uses the second input information τ₂ to correctly compute f(τ₂) with a probability greater than a certain probability to provide the result of the computation as second output information z₂, the computing apparatus generates a computation result u=f(x)^(b)x₁ from the first output information z₁ and generates a computation result v=f(x)^(a)x₂ from the second output information z₂ and, when the computation results u and v satisfy u^(a)=v^(b), outputs u^(b′)v^(a′) for integers a′ and b′ that satisfy a′a+b′b=1, where G and H are groups, f(x) is a decryption function for decrypting the ciphertext x which is an element of the group H with a particular decryption key to obtain an element of the group G, X₁ and X₂ are random variables having values in the group G, x₁ is a realization of the random variable X₁, x₂ is a realization of the random variable X₂, and a and b are natural numbers relatively prime to each other.

Effects of the Invention

According to the present invention, the capability providing apparatus provides only a computing capability to the computing apparatus without leaking secret information and the computing apparatus can use the computing capability to correctly perform computations.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a configuration of a proxy computing system of an embodiment;

FIG. 2 is a block diagram illustrating a configuration of a computing apparatus of an embodiment;

FIG. 3 is a block diagram illustrating a configuration of a capability providing apparatus of an embodiment;

FIG. 4 is a block diagram illustrating a configuration of an input information providing unit of an embodiment;

FIG. 5 is a block diagram illustrating a configuration of an input information providing unit of an embodiment;

FIG. 6 is a flowchart illustrating a process performed by a computing apparatus of an embodiment;

FIG. 7 is a flowchart illustrating a process performed by a capability providing apparatus of an embodiment;

FIG. 8 is a flowchart illustrating a process at step S2103 (S3103);

FIG. 9 is a flowchart illustrating a process at step S4103;

FIG. 10 is a block diagram illustrating a configuration of a computing apparatus of an embodiment;

FIG. 11 is a flowchart illustrating a process performed by a computing apparatus of an embodiment;

FIG. 12 is a block diagram illustrating a configuration of a computing apparatus of an embodiment;

FIG. 13 is a block diagram illustrating a configuration of a capability providing apparatus of an embodiment;

FIG. 14 is a block diagram illustrating a configuration of an input information providing unit of an embodiment;

FIG. 15 is a flowchart illustrating a process performed by a computing apparatus of an embodiment;

FIG. 16 is a flowchart illustrating an example of a process at step S6103;

FIG. 17 is a flowchart illustrating a process performed by a capability providing apparatus of an embodiment;

FIG. 18 is a block diagram illustrating a configuration of a proxy computing system of an embodiment;

FIG. 19 is a block diagram illustrating a configuration of a computing apparatus of an embodiment;

FIG. 20 is a block diagram illustrating a configuration of a capability providing apparatus of an embodiment;

FIG. 21 is a block diagram illustrating a configuration of a decryption control apparatus of an embodiment;

FIG. 22 is a block diagram illustrating a configuration of an input information providing unit of an embodiment;

FIG. 23 is a block diagram illustrating a configuration of an input information providing unit of an embodiment;

FIG. 24 is a flowchart illustrating an encryption process of an embodiment;

FIG. 25 is a flowchart illustrating a decryption process of an embodiment;

FIG. 26 is a flowchart illustrating a decryption capability providing process of an embodiment;

FIG. 27 is a flowchart illustrating a process at step S12103 (S13103);

FIG. 28 is a flowchart illustrating a process at step S14103;

FIG. 29 is a block diagram illustrating a configuration of a computing apparatus of an embodiment;

FIG. 30 is a flowchart illustrating a decryption process of an embodiment;

FIG. 31 is a block diagram illustrating a configuration of a proxy computing system of an embodiment;

FIG. 32 is a block diagram illustrating a configuration of a computing apparatus of an embodiment;

FIG. 33 is a block diagram illustrating a configuration of a capability providing apparatus of an embodiment;

FIG. 34 is a block diagram illustrating a configuration of an input information providing unit of an embodiment;

FIG. 35 is a block diagram illustrating a configuration of an input information providing unit of an embodiment;

FIG. 36 is a block diagram illustrating a configuration of an input information providing unit of an embodiment;

FIG. 37 is a flowchart illustrating a process performed by a computing apparatus of an embodiment;

FIG. 38 is a flowchart illustrating a process performed by a capability providing apparatus of an embodiment;

FIG. 39 is a flowchart illustrating a process at step S22103 (S23103);

FIG. 40 is a flowchart illustrating a process at step S24103;

FIG. 41 is a flowchart illustrating a process at step S25103;

FIG. 42 is a block diagram illustrating a configuration of an input information providing unit of an embodiment;

FIG. 43 is a flowchart illustrating a process at step S27103;

FIG. 44 is a block diagram illustrating a configuration of a computing apparatus of an embodiment; and

FIG. 45 is a flowchart illustrating a process performed by a computing apparatus of an embodiment.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Embodiments of the present invention will be described below with reference to drawings.

First Embodiment

A first embodiment of the present invention will be described.

<Configuration>

As illustrated in FIG. 1, a proxy computing system 1 of the first embodiment includes, for example, a computing apparatus 11 which does not have a decryption key and a capability providing apparatus 12 which has a decryption key. The computing apparatus 11 requests the capability providing apparatus 12 to provide a capability of decrypting a ciphertext and uses the capability of decryption provided from the capability providing apparatus 12 to decrypt the ciphertext. The computing apparatus 11 and the capability providing apparatus 12 are configured to be able to exchange information. For example, the computing apparatus 11 and the capability providing apparatus 12 are configured to be able to exchange information through a transmission line, a network, a portable recording medium and/or other medium.

As illustrated in FIG. 2, the computing apparatus 11 of the first embodiment includes, for example, a natural number storage 1101, a natural number selecting unit 1102, an integer computing unit 1103, an input information providing unit 1104, a first computing unit 1105, a first power computing unit 1106, a first list storage 1107, a second computing unit 1108, a second power computing unit 1109, a second list storage 1110, a determining unit 1111, a final output unit 1112, and a controller 1113. Examples of the computing apparatus 11 include a device having a computing function and a memory function, such as a card reader-writer apparatus and a mobile phone, and a well-known or specialized computer that includes a CPU (central processing unit) and a RAM (random-access memory) in which a special program is loaded.

As illustrated in FIG. 3, the capability providing apparatus 12 of the first embodiment includes, for example, a first output information computing unit 1201, a second output information computing unit 1202, a key storage 1204 and a controller 1205. Examples of the capability providing apparatus 12 include a tamper-resistant module such as an IC card and an IC chip, a device having computing and memory functions, such as a mobile phone, and a well-known or specialized computer including a CPU and a RAM in which a special program is loaded.

<Processes>

Processes of this embodiment will be described below. For the processes, let G and H be groups (for example commutative groups), f(x) be a decryption function for decrypting a ciphertext x, which is an element of the group H, with a particular decryption key s to obtain an element of the group G, generators of the groups G and H be μ_(g) and μ_(h), respectively, X₁ and X₂ be random variables having values in the group G, x₁ be a realization of the random variable X₁, and x₂ be a realization of the random variable X₂. It is assumed here that a plurality of pairs of natural numbers a, b that are relatively prime to each other (a, b) are stored in the natural number storage 1101 of the computing apparatus 11. The term “natural number” means an integer greater than or equal to 0. Let I be a set of pairs of relatively prime natural numbers that are less than the order of the group G, then it can be considered that pairs (a, b) of natural numbers a, b corresponding to a subset S of I are stored in the natural number storage 1101. It is also assumed that a particular decryption key s is stored in the key storage 1204 of the capability providing apparatus 12 in a secure manner. Processes of the computing apparatus 11 are performed under the control of the controller 1113 and processes of the capability providing apparatus 12 are performed under the control of the controller 1205.

As illustrated in FIG. 6, first, the natural number selecting unit 1102 of the computing apparatus 11 (FIG. 2) randomly reads one pair of natural numbers (a, b) from a plurality of pairs of natural numbers (a, b) stored in the natural number storage 1101. At least part of information on the read pair of natural numbers (a, b) is sent to the integer computing unit 1103, the input information providing unit 1104, the first power computing unit 1106, and the second power computing unit 1109 (step S1100).

The integer computing unit 1103 uses the sent pair of natural numbers (a, b) to compute integers a′, b′ that satisfy the relation a′a+b′b=1. Since the natural numbers a and b are relatively prime to each other, the integers a′ and b′ that satisfy the relation a′a+b′b=1 definitely exist. Information on the pair of natural numbers (a′, b′) is sent to the final output unit 1112 (step S1101).

The controller 1113 set t=1 (step S1102).

The input information providing unit 1104 generates and outputs first input information τ₁ and second input information τ₂ which are elements of the group H and each of which corresponds to the input ciphertext x. Preferably, each of the first input information τ₁ and the second input information τ₂ is information whose relation with the ciphertext x is scrambled. This enables the computing apparatus 11 to conceal the ciphertext x from the capability providing apparatus 12. Preferably, the first input information τ₁ of this embodiment further corresponds to the natural number b selected by the natural number selecting unit 1102 and the second input information τ₂ further corresponds to the natural number a selected by the natural number selecting unit 1102. This enables the computing apparatus 11 to evaluate the decryption capability provided by the capability providing apparatus 12 with a high degree of accuracy (step S1103).

As exemplified in FIG. 7, the first input information τ₁ is input in the first output information computing unit 1201 of the capability providing apparatus 12 (FIG. 3) and the second input information τ₂ is input in the second output information computing unit 1202 (step S1200).

The first output information computing unit 1201 uses the first input information τ₁ and the decryption key s stored in the key storage 1204 to correctly compute f(τ₁) with a probability greater than a certain probability and sets the result of the computation as first output information z₁ (step S1201). The second output information computing unit 1202 uses the second input information τ₂ and the decryption key s stored in the key storage 1204 to correctly compute f(τ₂) with a probability greater than a certain probability and sets the result of the computation as second output information z₂ (step S1202). Note that the “certain probability” is a probability less than 100%. An example of the “certain probability” is a nonnegligible probability and an example of the “nonnegligible probability” is a probability greater than or equal to 1/ψ(k), where ψ(k) is a polynomial that is a weakly increasing function (non-decreasing function) for a security parameter k. That is, the first output information computing unit 1201 and the second output information computing unit 1202 output computation results that have an intentional or unintentional error. In other words, the result of the computation by the first output information computing unit 1201 may or may not be f(τ₁) and the result of the computation by the second output information computing unit 1202 may or may not be f(τ₂).

The first output information computing unit 1201 outputs the first output information z₁ and the second output information computing unit 1202 outputs the second output information z₂ (step S1203).

Returning to FIG. 6, the first output information z₁ is input in the first computing unit 1105 of the computing apparatus 11 (FIG. 2) and the second output information z₂ is input in the second computing unit 1108. The first output information z₁ and the second output information z₂ are equivalent to the decryption capability provided by the capability providing apparatus 12 to the computing apparatus 11 (step S1104).

The first computing unit 1105 generates a computation result u=f(x)^(b)x₁ from the first output information z₁. Here, generating (computing) f(x)^(b)x₁ means computing a value of a formula defined as f(x)^(b)x₁. Any intermediate computation method may be used, provided that the value of the formula f(x)^(b)x₁ can be eventually computed. The same applies to computations of the other formulae that appear herein. In the first embodiment, a computation defined at a group is expressed multiplicatively. That is, “α^(b)” for αϵG means that a computation defined at the group G is applied b times to α and “α₁α₂” for α₁, α₂ϵG means that a computation defined at the group G is performed on operands α₁ and α₂ (the same applies to second to fifth embodiments described later). The result u of the computation is sent to the first power computing unit 1106 (step S1105).

The first power computing unit 1106 computes u′=u^(a). The pair of the result u of the computation and u′ computed on the basis of the result of the computation, (u, u′), is stored in the first list storage 1107 (step S1106).

The determining unit 1111 determines whether or not there is one that satisfies u′=v′ among the pairs (u, u′) stored in the first list storage 1107 and the pairs (v, v′) stored in the second list storage 1110 (step S1107). If no pair (v, v′) is stored in the second list storage 1110, the process at step S1107 is omitted and a process at step S1108 is performed. If there is one that satisfies u′=v′, the process proceeds to step S1114; if there is not one that satisfies u′=v′, the process proceeds to step S1108.

At step S1108, the second computing unit 1108 generates a computation result v=f(x)^(a)x₂ from the second output information z₂. The result v of the computation is sent to the second power computing unit 1109 (step S1108).

The second power computing unit 1109 computes v′=v^(b). The pair (v, v′) of the result v of the computation and v′ computed on the basis of the computation result is stored in the second list storage 1110 (step S1109).

The determining unit 1111 determines whether or not there is one that satisfies u′=v′ among the pairs (u, u′) stored in the first list storage 1107 and the pairs (v, v′) stored in the second list storage 1110 (step S1110). If there is one that satisfies u′=v′, the process proceeds to step S1114. If there is not one that satisfies u′=v′, the process proceeds to step S1111.

At step S1111, the controller 1113 determines whether or not t=T_(max) (step S1111). Here, T_(max) is a predetermined natural number. If t=T_(max), the controller 1113 outputs information indicating that the computation is impossible, for example the symbol “⊥” (step S1113) and the process ends. If not t=T_(max), the controller 1113 increments t by 1, that is, sets t=t+1 (step S1112) and the process returns to step S1103.

The information indicating the computation is impossible (the symbol “⊥” in this example) means that the level of reliability that the capability providing apparatus 12 correctly performs computation is lower than a criterion defined by T_(max). In other words, the capability providing apparatus 12 was unable to perform a correct computation in T_(max) trials.

At step S1114, the final output unit 1112 uses u and v that correspond to u′ and v′ that are determined to satisfy u′=v′ to calculate and output u^(b′)v^(a′) (step S1114). The u^(b′)v^(a′) thus computed will be a result f(x) of decryption of the ciphertext x with the particular decryption key s with a high probability (the reason why u^(b′)v^(a′)=f(x) with a high probability will be described later). Therefore, the process described above is repeated multiple times and the value obtained with the highest frequency among the values obtained at step S1114 can be provided as the result of decryption. As will be described, u^(b′)v^(a′)=f(x) can result with an overwhelming probability, depending on settings. In that case, the value obtained at step S1114 can be directly provided as the result of decryption.

<<Reason why u^(b′)v^(a′)=f(x) with a High Probability>>

Let X be a random variable having a value in the group G. For wϵG, an entity that returns wx′ corresponding to a sample x′ according to the random variable X in response to each request is called a sampler having an error X for w.

For wϵG, an entity that returns w^(a)x′ corresponding to a sample x′ according to a random variable X whenever a natural number a is given is called a randomizable sampler having an error X for w. The randomizable sampler functions as the sampler if used with a=1.

The combination of the input information providing unit 1104, the first output information computing unit 1201 and the first computing unit 1105 of this embodiment is a randomizable sampler having an error X₁ for f(x) (referred to as the “first randomizable sampler”) and the combination of the input information providing unit 1104, the second output information computing unit 1202 and the second computing unit 1108 is a randomizable sampler having an error X₂ for f(x) (referred to as the “second randomizable sampler”).

The inventor has found that if u′=v′ holds, that is, if u^(a)=v^(b) holds, it is highly probable that the first randomizable sampler has correctly computed u=f(x)^(b) and the second randomizable sampler has correctly computed v=f(x)^(a) (x₁ and x₂ are identity elements e_(g) of the group G). For simplicity of explanation, this will be proven in a fifth embodiment.

When the first randomizable sampler correctly computes u=f(x)^(b) and the second randomizable sampler correctly computes v=f(x)^(a) (when x₁ and x₂ are identity elements e_(g) of the group G), then u^(b′)v^(a′)=(f(x)^(b)x₁)^(b′)(f(x)^(a)x₂)^(a′)=(f(x)^(b)e_(g))^(b′)(f(x)^(a)e_(g))^(a′)=f(x)^(bb′)e_(g) ^(b′)f(x)^(aa′)e_(g) ^(a′)=f(x)^((bb′+aa′))=f(x).

For (q₁, q₂)ϵ1, a function π_(i) is defined by π_(i)(q₁, q₂)=q_(i) for each of i=1, 2. Let L=min (#π₁(S), #π₂(S)), where #⋅ is the order of a set ⋅. If the group G is a cyclic group or a group whose order is difficult to compute, it can be expected that the probability that an output other than “⊥” of the computing apparatus 11 is not f(x) is at most approximately T_(max) ² L/#S within a negligible error. If L/#S is a negligible quantity and T_(max) is a quantity approximately equal to a polynomial order, the computing apparatus 11 outputs a correct f(x) with an overwhelming probability. An example of S that results in a negligible quantity of L/#S is S={(1, d)|dϵ[2, |G|−1]}, for example.

Second Embodiment

A proxy computing system of a second embodiment is an example that embodies the first randomizable sampler and the second randomizable sampler described above. The following description will focus on differences from the first embodiment and repeated description of commonalities with the first embodiment will be omitted. In the following description, elements labeled with the same reference numerals have the same functions and the steps labeled with the same reference numerals represent the same processes.

<Configuration>

As illustrated in FIG. 1, the proxy computing system 2 of the second embodiment includes a computing apparatus 21 in place of the computing apparatus 11 and a capability providing apparatus 22 in place of the capability providing apparatus 12.

As illustrated in FIG. 2, the computing apparatus 21 of the second embodiment includes, for example, a natural number storage 1101, a natural number selecting unit 1102, an integer computing unit 1103, an input information providing unit 2104, a first computing unit 2105, a first power computing unit 1106, a first list storage 1107, a second computing unit 2108, a second power computing unit 1109, a second list storage 1110, a determining unit 1111, a final output unit 1112 and a controller 1113. As illustrated in FIG. 4, the input information providing unit 2104 of this embodiment includes, for example, a first random number generator 2104 a, a first input information computing unit 2104 b, a second random number generator 2104 c, and a second input information computing unit 2104 d.

As illustrated in FIG. 3, the capability providing apparatus 22 of the second embodiment includes, for example, a first output information computing unit 2201, a second output information computing unit 2202, a key storage 1204, and a controller 1205.

<Processes>

Processes of this embodiment will be described below. In the second embodiment, a decryption function f(x) is a homomorphic function, a group H is a cyclic group, and a generator of the group H is μ_(h), the order of the group H is K_(H), and ν=f(μ_(h)). The rest of the assumptions are the same as those in the first embodiment, except that the computing apparatus 11 is replaced with the computing apparatus 21 and the capability providing apparatus 12 is replaced with the capability providing apparatus 22.

As illustrated in FIGS. 6 and 7, a process of the second embodiment is the same as the process of the first embodiment except that steps S1103 through S1105, S1108, and S1200 through S1203 of the first embodiment are replaced with steps S2103 through S2105, S2108, and S2200 through S2203, respectively. In the following, only processes at steps S2103 through S2105, S2108, and S2200 through S2203 will be described.

<<Process at Step S2103>>

The input information providing unit 2104 of the computing apparatus 21 (FIG. 2) generates and outputs first input information τ₁ and second input information τ₂ which are elements of the group H and each of which corresponds to an input ciphertext x (Step S2103 of FIG. 6). A process at step S2103 of this embodiment will be described with reference to FIG. 8.

The first random number generator 2104 a (FIG. 4) generates a uniform random number r₁ that is a natural number greater than or equal to 0 and less than K_(H). The generated random number r₁ is sent to the first input information computing unit 2104 b and the first computing unit 2105 (step S2103 a). The first input information computing unit 2104 b uses the input random number r₁, the ciphertext x and a natural number b to compute first input information τ₁=μ_(h) ^(r1)x^(b) (step S2103 b). Here, the superscript r1 on μ₁, represents r₁. When a notation α^(βγ) is used herein in this way, βγ represents β_(γ), namely β with subscript γ, where α is a first letter, β is a second letter, and γ is a number.

The second random number generator 2104 c generates a uniform random number r₂ that is a natural number greater than or equal to 0 and less than K_(II). The generated random number r₂ is sent to the second input information computing unit 2104 d and the second computing unit 2108 (step S2103 c). The second input information computing unit 2104 d uses the input random number r₂, the ciphertext x, and a natural number a to compute second input information τ₂=μ_(h) ^(r2)x^(a) (step S2103 d).

The first input information computing unit 2104 b and the second input information computing unit 2104 d output the first input information τ₁ and the second input information τ₂ thus generated (step S2103 e). Note that the first input information τ₁ and the second input information τ₂ in this embodiment are information whose relation with the ciphertext x is scrambled using random numbers r₁, r₂, respectively. This enables the computing apparatus 21 to conceal the ciphertext x from the capability providing apparatus 22. The first input information τ₁ of this embodiment further corresponds to the natural number b selected by the natural number selecting unit 1102 and the second input information τ₂ further corresponds to the natural number a selected by the natural number selecting unit 1102. This enables the computing apparatus 21 to evaluate the decryption capability provided by the capability providing apparatus 22 with a high degree of accuracy.

<<Processes at Steps S2200 Through S2203>>

As illustrated in FIG. 7, first, the first input information τ₁=μ_(h) ^(r1)x^(b) is input in the first output information computing unit 2201 of the capability providing apparatus 22 (FIG. 3) and the second input information τ₂=μ_(h) ^(r2)x^(a) is input in the second output information computing unit 2202 (step S2200).

The first output information computing unit 2201 uses the first input information τ₁=μ_(h) ^(r1)x^(b) and a decryption key s stored in the key storage 1204 to correctly compute f(μ_(h) ^(r1)x^(b)) with a probability greater than a certain probability and sets the result of the computation as first output information z₁. The result of the computation may or may not be correct. That is, the result of the computation by the first output information computing unit 2201 may or may not be f(μ_(h) ^(r1)x^(b)) step S2201).

The second output information computing unit 2202 uses the second input information τ₂=μ_(h) ^(r2)x^(a) and the decryption key s stored in the key storage 1204 to correctly compute f(μ_(h) ^(r2)x^(a)) with a probability greater than a certain probability and sets the result of the computation as second output information z₂. The result of the computation may or may not be correct. That is, the result of the computation by the second output information computing unit 2202 may or may not be f(μ_(h) ^(r2)x^(a))(step S2202). The first output information computing unit 2201 outputs the first output information z₁ and the second output information computing unit 2202 outputs the second output information z₂ (step S2203).

<<Processes at Steps S2104 and S2105>>

Returning to FIG. 6, the first output information z₁ is input in the first computing unit 2105 of the computing apparatus 21 (FIG. 2) and the second output information z₂ is input in the second computing unit 2108. The first output information z₁ and the second output information z₂ are equivalent to the decryption capability provided by the capability providing apparatus 22 to the computing apparatus 21 (step S2104).

The first computing unit 2105 uses the input random number r₁ and the first output information z₁ to compute z₁ν^(−r1) and sets the result of the computation as u. The result u of the computation is sent to the first power computing unit 1106. Here, u=z₁ν^(−r1)=f(x)^(b)x₁. That is, z₁ν^(−r1) is an output of a randomizable sampler having an error X₁ for f(x). The reason will be described later (step S2105).

<<Process at Step S2108>>

The second computing unit 2108 uses the input random number r₂ and the second output information z₂ to compute z₂ν^(−r2) and sets the result of the computation as v. The result v of the computation is sent to the second power computing unit 1109. Here, v=z₂ν^(−r2)=f(x)^(a)x₂. That is, z₂ν^(−r2) is an output of a randomizable sampler having an error X₂ for f(x). The reason will be described later (step S2108).

<<Reason Why z₁ν^(−r1) and z₂ν^(−r2) are Outputs of Randomizable Samplers Having Errors X₁ and X₂, Respectively, for f(x)>>

Let c be a natural number, R and R′ be random numbers, and B(μ_(h) ^(R)x^(c)) be the result of computation performed by the capability providing apparatus 22 using μ_(h) ^(R)x^(c). That is, the results of computations that the first output information computing unit 2201 and the second output information computing unit 2202 return to the computing apparatus 21 are z=B(μ_(h) ^(R)x_(c)). A random variable X that has a value in the group G is defined as X=B(μ_(h) ^(R′))f(μ_(h) ^(R′))⁻¹.

Then, zν^(−R)=B(μ_(h) ^(R)x^(c))f(μ_(h))^(−R)=Xf(μ_(h) ^(R)x^(c))f(μ_(h))^(−R)=Xf(μ_(h))^(R)f(X)^(c)f(μ_(h)) ^(−R)=f(x)^(c)X. That is, zν^(−R) is an output of a randomizable sampler having an error X for f(x).

The expansion of formula given above uses the properties such that X=B(μ_(h) ^(R′))f(μ_(h) ^(R′))⁻¹=B(μ_(h) ^(R)x^(c))f(μ_(h) ^(R)x^(c))⁻¹ and that B(μ_(h) ^(R)x^(c))=Xf(μ_(h) ^(R)x^(c)). The properties are based on the fact that the function f(x) is a homomorphic function and R and R′ are random numbers.

Therefore, considering that a and b are natural numbers and r₁ and r₂ are random numbers, z₁ν^(−r1) and z₂ν^(−r2) are, likewise, outputs of randomizable samplers having errors X₁ and X₂, respectively, for f(x).

Third Embodiment

A third embodiment is a variation of the second embodiment and computes a value of u or v by using samplers described above when a=1 or b=1. The amounts of computations performed by samplers are in general smaller than the amounts of computations by randomizable samplers. Using samplers instead of randomizable samplers for computations when a=1 or b=1 can reduce the amounts of computations by a proxy computing system. The following description will focus on differences from the first and second embodiments and repeated description of commonalities with the first and second embodiments will be omitted.

<Configuration>

As illustrated in FIG. 1, a proxy computing system 3 of the third embodiment includes a computing apparatus 31 in place of the computing apparatus 21 and a capability providing apparatus 32 in place of the capability providing apparatus 22.

As illustrated in FIG. 2, the computing apparatus 31 of the third embodiment includes, for example, a natural number storage 1101, a natural number selecting unit 1102, an integer computing unit 1103, an input information providing unit 3104, a first computing unit 2105, a first power computing unit 1106, a first list storage 1107, a second computing unit 2108, a second power computing unit 1109, a second list storage 1110, a determining unit 1111, a final output unit 1112, a controller 1113, and a third computing unit 3109.

As illustrated in FIG. 3, the capability providing apparatus 32 of the third embodiment includes, for example, a first output information computing unit 2201, a second output information computing unit 2202, a key storage 1204, a controller 1205, and a third output information computing unit 3203.

<Processes>

Processes of this embodiment will be described below. Differences from the second embodiment will be described.

As illustrated in FIGS. 6 and 7, a process of the third embodiment is the same as the process of the second embodiment except that steps S2103 through S2105, S2108, and S2200 through S2203 of the second embodiment are replaced with steps S3103 through S3105, S3108, S3200 through S3203, and S3205 through 3209, respectively. The following description will focus on processes at steps S3103 through S3105, S3108, S3200 through S3203, and S3205 through S3209.

<<Process at Step S3103>>

The input information providing unit 3104 of the computing apparatus 31 (FIG. 2) generates and outputs first input information τ₁ and second input information τ₂ which are elements of a group H and each of which corresponds to an input ciphertext x (step S3103 of FIG. 6).

A process at step S3103 of this embodiment will be described below with reference to FIG. 8.

The controller 1113 (FIG. 2) controls the input information providing unit 3104 according to natural numbers (a, b) selected by the natural number selecting unit 1102.

Determination is made by the controller 1113 as to whether b is equal to 1 (step S3103 a). If it is determined that b≠1, the processes at steps S2103 a and 2103 b described above are performed and the process proceeds to step S3103 g.

On the other hand, if it is determined at step S3103 a that b=1, the third random number generator 3104 e generates a random number r₃ that is a natural number greater than or equal to 0 and less than K_(H). The generated random number r₃ is sent to the third input information computing unit 3104 f and the third computing unit 3109 (step S3103 b). The third input information computing unit 3104 f uses the input random number r₃ and a ciphertext x to compute x^(r3) and sets x^(r3) as first input information τ₁ (step S3103 c). Then the process proceeds to step S3103 g.

At step S3103 g, determination is made by the controller 1113 as to whether a is equal to 1 (step S3103 g). If it is determined that a≠1, the processes at steps S2103 c and S2103 d described above are performed.

On the other hand, if it is determined at step S3103 g that a=1, the third random number generator 3104 e generates a random number r₃ that is a natural number greater than or equal to 0 and less than K_(H). The generated random number r₃ is sent to the third input information computing unit 3104 f (step S3103 h). The third input information computing unit 3104 f uses the input random number r₃ and the ciphertext x to compute x^(r3) and sets x^(r3) as second input information τ₂ (step S3103 i).

The first input information computing unit 2104 b, the second input information computing unit 2104 d, and the third input information computing unit 3104 f output the first input information τ₁ and the second input information τ₂ thus generated along with information on the corresponding natural numbers (a, b) (step S3103 e). Note that the first input information τ₁ and the second input information τ₂ in this embodiment are information whose relation with the ciphertext x is scrambled using random numbers r₁, r₂ and r₃. This enables the computing apparatus 31 to conceal the ciphertext x from the capability providing apparatus 32.

<<Processes at S3200 Through S3203 and S3205 Through S3209>>

Processes at S3200 through S3203 and S3205 through S3209 of this embodiment will be described below with reference to FIG. 7.

The controller 1205 (FIG. 3) controls the first output information computing unit 2201, the second output information computing unit 2202 and the third output information computing unit 3203 according to input natural numbers (a, b).

Under the control of the controller 1205, the first input information τ₁=μ_(h) ^(r1)x^(b) when b≠1 is input in the first output information computing unit 2201 of the capability providing apparatus 32 (FIG. 3) and the second input information τ₂=μ_(h) ^(r2)x^(a) when a≠1 is input in the second output information computing unit 2202. The first input information τ₁=x^(r3) when b=1 and the second input information τ₂=x^(r3) when a=1 are input in the third output information computing unit 3203 (step S3200).

Determination is made by the controller 1113 as to whether b is equal to 1 (step S3205). If it is determined that b≠1, the process at step S2201 described above is performed. Then, determination is made by the controller 1113 as to whether a is equal to 1 (step S3208). If it is determined that a≠1, the process at step S2202 described above is performed and then the process proceeds to step S3203.

On the other hand, if it is determined at step S3208 that a=1, the third output information computing unit 3203 uses the second input information τ₂=x^(r3) to correctly compute f(x^(r3)) with a probability greater than a certain probability and sets the obtained result of the computation as third output information z₃. The result of the computation may or may not be correct. That is, the result of the computation by the third output information computing unit 3203 may or may not be f(x^(r3)) (step S3209). Then the process proceeds to step S3203.

If it is determined at step S3205 that b=1, the third output information computing unit 3203 uses the first input information τ₁=x^(r3) to correctly compute f(x^(r3)) with a probability greater than a certain probability and sets the obtained result of the computation as third output information z₃. The result of the computation may or may not be correct. That is, the result of the computation by the third output information computing unit 3203 may or may not be f(x^(r3)) (step S3206).

Then, determination is made by the controller 1113 as to whether a is equal to 1 (step S3207). If it is determined that a=1, the process proceeds to step S3203; if it is determined that a≠1, the process proceeds to step S2202.

At step S3203, the first output information computing unit 2201, which has generated the first output information z₁, outputs the first output information z₁, the second output information computing unit 2202, which has generated the second output information z₂, outputs the second output information z₂, and the third output information computing unit 3202, which has generated the third output information z₃, outputs the third output information z₃ (step S3203).

<<Processes at Steps S3104 and S3105>>

Returning to FIG. 6, under the control of the controller 1113, the first output information z₁ is input in the first computing unit 2105 of the computing apparatus 31 (FIG. 2), the second output information z₂ is input in the second computing unit 2108, and the third output information z₃ is input in the third computing unit 3109 (step S3104).

If b≠1, the first computing unit 2105 performs the process at step S2105 described above to generate u; if b=1, the third computing unit 3109 computes z₃ ^(1/r3) and sets the result of the computation as u. The result u of the computation is sent to the first power computing unit 1106. Here, if b=1, then u=z₃ ^(1/r3)=f(x)x₃. That is, z₃ ^(1/r3) serves as a sampler having an error X₃ for f(x). The reason will be described later (step S3105).

<<Process at Step S3108>>

If a≠1, the second computing unit 2108 performs the process at S2108 described above to generate v; if a=1, the third computing unit 3109 computes z₃ ^(1/r3) and sets the result of the computation as v. The result v of the computation is sent to the second power computing unit 1109. Here, if a=1, then v=z₃ ^(1/r3)=f(x)x₃. That is, z₃ ^(1/r3) serves as a sampler having an error X₃ for f(x). The reason will be described later (step S3108).

Note that if z₃ ^(1/r3), that is, the radical root of z₃, is hard to compute, u and/or v may be calculated as follows. The third computing unit 3109 may store each pair of random number r₃ and z₃ computed on the basis of the random number r₃ in a storage, not depicted, in sequence as (α₁, β₁), (α₂, β₂), . . . , (α_(m), (β_(m)), . . . . Here, in is a natural number. The third computing unit 3109 may compute γ₁, γ₂, . . . , γ_(m) that satisfies γ₁α₁+γ₂α₂+ . . . +γ_(m)α_(m)=1 when the least common multiple of α₁, α₂, . . . , α_(m) is 1, where γ₁, γ₂, . . . , γ_(m) are integers. The third computing unit 3109 may then use the resulting γ₁, γ₂, . . . , γ_(m) to compute Π_(i=1) ^(m)β_(i) ^(γi)=β₁ ^(γ1)β₂ ^(γ2) . . . β_(m) ^(γm) and may set the results of the computation as u and/or v.

<<Reason why z₃ ^(1/r3) Serves as a Sampler Having an Error X₃ for f(x)>>

Let R be a random number and B(x^(R)) be the result of computation performed by the capability providing apparatus 32 using x^(R). That is, let z=B(x^(R)) be the results of computations returned by the first output information computing unit 2201, the second output information computing unit 2202, and the third output information computing unit 3203 to the computing apparatus 31. Furthermore, a random variable X having a value in the group G is defined as X=B(x^(R))^(1/R)f(x)⁻¹.

Then, z^(1/R)=B(x^(R))^(1/R)=Xf(x)=f(x)X. That is, z^(1/R) serves as a sampler having an error X for f(x).

The expansion of formula given above uses the properties such that X=B(x^(R))^(1/R)f(x^(R))⁻¹ and that B(x^(R))^(1/R)=Xf(x^(R)). The properties are based on the fact that R is a random number.

Therefore, considering that r₃ is a random number, Z^(1/R) serves as a sampler having an error X₃ for f(x) likewise.

Fourth Embodiment

A proxy computing system of a fourth embodiment is another example that embodies the first and second randomizable samplers described above. Specifically, the proxy computing system embodies an example of first and second randomizable samplers in the case where H=G×G, the decryption function f(x) is a decryption function of ElGamal encryption, that is, f(c₁, c₂)=c₁c₂ ^(−s) for a decryption key s and a ciphertext x=(c₁, c₂). The following description will focus on differences from the first embodiment and repeated description of commonalities with the first embodiment will be omitted.

As illustrated in FIG. 1, the proxy computing system 4 of the fourth embodiment includes a computing apparatus 41 in place of the computing apparatus 11 and a capability providing apparatus 42 in place of the capability providing apparatus 12.

As illustrated in FIG. 2, the computing apparatus 41 of the fourth embodiment includes, for example, a natural number storage 1101, a natural number selecting unit 1102, an integer computing unit 1103, an input information providing unit 4104, a first computing unit 4105, a first power computing unit 1106, a first list storage 1107, a second computing unit 4108, a second power computing unit 1109, a second list storage 1110, a determining unit 1111, a final output unit 1112, and a controller 1113. As illustrated in FIG. 5, the input information providing unit 4104 of this embodiment includes, for example, a fourth random number generator 4104 a, a fifth random number generator 4104 b, a first input information computing unit 4104 c, a sixth random number generator 4104 d, a seventh random number generator 4104 e, and a second input information computing unit 4104 f. The first input information computing unit 4104 c includes, for example, a fourth input information computing unit 4104 ca and a fifth input information computing unit 4104 cb. The second input information computing unit 4104 f includes, for example, a sixth input information computing unit 4104 fa and a seventh input information computing unit 4104 fb.

As illustrated in FIG. 3, the capability providing apparatus 42 of the fourth embodiment includes, for example, a first output information computing unit 4201, a second output information computing unit 4202, a key storage 1204, and a controller 1205.

<Processes>

Processes of this embodiment will be described below. In the fourth embodiment, it is assumed that a group H is the direct product group G×G of a group G, the group G is a cyclic group, a ciphertext x=(c₁, c₂)ϵH, f(c₁, c₂) is a homomorphic function, a generator of the group G is μ_(g), the order of the group G is K_(G), a pair of a ciphertext (V, W)ϵH and a text f(V, W)=YϵG decrypted from the ciphertext for the same decryption key s is preset in the computing apparatus 41 and the capability providing apparatus 42, and the computing apparatus 41 and the capability providing apparatus 42 can use the pair.

As illustrated in FIGS. 6 and 7, a process of the fourth embodiment is the same as the process of the first embodiment except that steps S1103 through S1105, S1108, and S1200 through S1203 of the first embodiment are replaced with steps S4103 through S4105, S4108, and S4200 through S4203, respectively. In the following, only processes at steps S4103 through S4105, S4108, and S4200 through S4203 will be described.

<<Process at Step S4103>>

The input information providing unit 4104 of the computing apparatus 41 (FIG. 2) generates and outputs first input information τ₁ which is an element of the group H and corresponds to an input ciphertext x=(c₁, c₂) and second input information τ₂ which is an element of the group H and corresponds to the ciphertext x=(c₁, c₂) (step S4103 of FIG. 6). A process at step S4103 of this embodiment will be described below with reference to FIG. 9.

The fourth random number generator 4104 a (FIG. 5) generates a uniform random number r₄ that is a natural number greater than or equal to 0 and less than K_(G). The generated random number r₄ is sent to the fourth input information computing unit 4104 ca, the fifth input information computing unit 4104 cb, and the first computing unit 4105 (step S4103 a). The fifth random number generator 4104 b generates a uniform random number r₅ that is a natural number greater than or equal to 0 and less than K_(G). The generated random number r₅ is sent to the fifth input information computing unit 4104 cb and the first computing unit 4105 (step S4103 b).

The fourth input information computing unit 4104 ca uses a natural number b selected by the natural number selecting unit 1102, c₂ included in the ciphertext x, and the random number r₄ to compute fourth input information c₂ ^(b)W^(r4) (step S4103 c). The fifth input information computing unit 4104 cb uses the natural number b selected by the natural number selecting unit 1102, c₁ included in the ciphertext x, and random numbers r₄ and r₅ to compute fifth input information c₁ ^(b)V^(r4)μ_(g) ^(r5) (step S4103 d).

The sixth random number generator 4104 d generates a uniform random number r₆ that is a natural number greater than or equal to 0 and less than K_(G). The generated random number r₆ is sent to the sixth input information computing unit 4104 fa, the seventh input information computing unit 4104 fb, and the second computing unit 4108 (step S4103 e). The seventh random number generator 4104 e generates a uniform random number r₇ that is a natural number greater than or equal to 0 and less than K_(G). The generated random number r₇ is sent to the sixth input information computing unit 4104 fa and the second computing unit 4108 (step S4103 f).

The sixth input information computing unit 4104 fa uses a natural number a selected by the natural number electing unit 1102, c₂ included in the ciphertext x, and the random number r₆ to compute sixth input information c₂ ^(a)W^(r6) (step S4103 g). The seventh input information computing unit 4104 fb uses the natural number a selected by the natural number selecting unit 1102, c₁ included in the ciphertext x, and the random number r₇ to compute seventh input information c₁ ^(a)V^(r6)μ_(g) ^(r7) (step S4103 h).

The first input information computing unit 4104 c outputs the fourth input information c₂ ^(b)W^(r4) and the fifth input information c₁ ^(a)V^(r6)μ_(g) ^(r5) generated as described above as first input information τ₁=(c₂ ^(b)W^(r4), c₁ ^(b)V^(r4)μ_(g) ^(r5)). The second input information computing unit 4104 f outputs the sixth input information c₂ ^(a)W^(r6) and the seventh input information c₁ ^(a)V^(r6)μ_(g) ^(r7) generated as described above as second input information τ₂=(c₂ ^(a)W^(r6), c₁ ^(a)V^(r6)μ_(g) ^(r7)) (step S4103 i).

<<Processes at Steps S4200 Through S4203>>

As illustrated in FIG. 7, first, the first input information τ₁=(c₂ ^(b)W^(r4), c₁ ^(b)V^(r4)μ_(g) ^(r5)) is input in the first output information computing unit 4201 of the capability providing apparatus 42 (FIG. 3) and the second input information τ₂=(c₂ ^(a)W^(r6), c₁ ^(a)V^(r6)μ_(g) ^(r7)) is input in the second output information computing unit 4202 (step S4200).

The first output information computing unit 4201 uses the first input information τ₁=(c₂ ^(b)W^(r4), c₁ ^(b)V^(r4)μ_(g) ^(r5)) and the decryption key s stored in the key storage 1204 to correctly compute f(c₁ ^(b)W^(r4) μ_(g) ^(r5), c₂ ^(b)W^(r4)) with a probability greater than a certain probability and sets the result of the computation as first output information z₁. The result of the computation may or may not be correct. That is, the result of the computation by the first output information computing unit 4201 may or may not be f(c₁ ^(b)V^(r4)μ_(g) ^(r5), c₂ ^(b)W^(r4)) (step S4201).

The second output information computing unit 4202 can correctly compute f(c₁ ^(a)V^(r6)μ_(g) ^(r7), c₂ ^(a)W^(r6)) with a probability greater than a certain probability by using the second input information τ₂=(c₂ ^(a)W^(r6), c₁ ^(a)V^(r6)μ_(g) ^(r7)) and the decryption key s stored in the key storage 1204 and sets the result of the computation as second output information z₂. The result of the computation may or may not be correct. That is, the result of the computation by the second output information computing unit 4202 may or may not be f(c₁ ^(a)V^(r6)μ_(g) ^(r7), c₂ ^(a)W^(r6))(step S4202). The first output information computing unit 4201 outputs the first output information z₁ and the second output information computing unit 4202 outputs the second output information z₂ (step S4203).

<<Processes at Steps S4104 and S4105>>

Returning to FIG. 6, the first output information z₁ is input in the first computing unit 4105 of the computing apparatus 41 (FIG. 2) and the second output information z₂ is input in the second computing unit 4108 (step S4104).

The first computing unit 4105 uses the input first output information z₁ and random numbers r₄ and r₅ to compute z₁Y^(−r4)μ_(g) ^(−r5) and sets the result of the computation as u (step S4105). The result u of the computation is sent to the first power computing unit 1106. Here, u=Z Y^(−r4)μ_(g) ^(−r5)=f(c₁, c₂)^(b)x₁. That is, z₁Y^(−r4) μ_(g) ^(−r5) is an output of a randomizable sampler having an error X₁ for f(c₁, c₂). The reason will be described later.

<<Process at Step S4108>>

The second computing unit 4108 uses the input second output information z₂ and random numbers r₆ and r₇ to compute Z₂Y^(−r6)μ_(g) ^(−r7) and sets the result of the computation as v. The result v of the computation is sent to the second power computing unit 1109. Here, v=z₂Y^(−r6) μ_(g) ^(−r7)=f(c₁,c₂)^(a)x₂. That is, Z₂Y^(−r6)μ^(−r7) is an output of a randomizable sampler having an error X₂ for f(c₁, c₂). The reason will be described later.

<<Reason why z₁Y^(−r4)μ_(g) ^(−r5) and z₂Y^(−r6)μ_(g) ^(−r7) are Outputs of Randomizable Samplers Having Errors X₁ and X₂, Respectively, for f(c₁, c₂)>>

Let c be a natural number, R₁, R₂, R₁′ and R₂′ be random numbers, and B(c₁ ^(c)V^(R1)μ_(g) ^(R2), c₂ ^(c)W^(R1)) be the result of computation performed by the capability providing apparatus 42 using c₁ ^(c)V^(R1)μ_(g) ^(R2) and c₂ ^(c)W^(R1). That is, the first output information computing unit 4201 and the second output information computing unit 4202 return z=B(c₁ ^(c)V^(R1) μ_(g) ^(R2), c₂ ^(c)W^(R1)) as the results of computations to the computing apparatus 41. Furthermore, a random variable X having a value in a group G is defined as X=B(V^(R1′)μ_(g) ^(R2′), W^(R1′))f(V^(R1′)μ_(g) ^(R2′), W^(R1′))⁻¹.

Here, zY^(−R1) μ_(g) ^(−R2)=B(c₁ ^(c)V^(R1)μ_(g) ^(R2), c₂ ^(c)W^(R1))Y−^(R1)μ_(g) ^(−R2)=Xf(c₁ ^(c)V^(R1)μ_(g) ^(R2), c₂ ^(c)W^(R1))Y^(−R1) μ_(g) ^(−R2)=Xf(c₁, c₂)^(c)f(V, W)^(R1)f(μ_(g), e_(g))^(R2)Y^(−R1) μ_(g) ^(−R2)=Xf(c₁, c₂)^(c)Y^(R1) μ_(g) ^(R2)Y^(−R1) μ_(g) ^(−R2)=f(c₁, c₂)^(c)X. That is, zY^(−R1) μ_(g) ^(−R2) is an output of a randomizable sampler having an error X for f(x). Note that e_(g) is an identity element of the group G.

The expansion of formula given above uses the properties such that X=B(V^(R1′)μ_(g) ^(R2′), W^(R1′))f(V^(R1′)μ_(g) ^(R2′), W^(R1′))⁻¹=B(c_(i) ^(c)V^(R1) μ_(g) ^(R2), c₂ ^(c)W^(R1))f(c₁ ^(c)V^(R1) μ_(g) ^(R2), c₂ ^(c)W^(R1)) and that B(c₁ ^(c)V^(R1) μ_(g) ^(R2), c₂ ^(c)W^(R1))=Xf(c₁ ^(c)V^(R1) μ_(g) ^(R2), c₂ ^(c)W^(R1)). The properties are based on the fact that R₁, R₂, R₁′ and R₂′ are random numbers.

Therefore, considering that a and b are natural numbers and r₄, r₅, r₆ and r₇ are random numbers, z₁Y^(−r4)μ_(g) ^(−r5) and z₂Y^(−r6)μ_(g) ^(−r7) are, likewise, outputs of randomizable samplers having errors X₁ and X₂, respectively, for f(c₁, c₂).

Fifth Embodiment

In the embodiments described above, a plurality of pairs (a, b) of natural numbers a and b that are relatively prime to each other are stored in the natural number storage 1101 of the computing apparatus and the pairs (a, b) are used to perform the processes. However, one of a and b may be a constant. For example, a may be fixed at 1 or b may be fixed at 1. In other words, one of the first randomizable sampler and the second randomizable sampler may be replaced with a sampler. If one of a and b is a constant, the process for selecting the constant a or b is unnecessary, the constant a or b is not input in the processing units and each processing units can treat it as a constant in computations. If a or b set as a constant is equal to 1, f(x)=u^(b′)V^(a′) can be obtained as f(x)=v or f(x)=u without using a′ or b′.

A fifth embodiment is an example of such a variation, in which b is fixed at 1 and the second randomizable sampler is replaced with a sampler. The following description will focus on differences from the first embodiment. Specific examples of the first randomizable sampler and the sampler are similar to those described in the second to fourth embodiments and therefore description of the first randomizable sampler and the sampler will be omitted.

<Configuration>

As illustrated in FIG. 1, a proxy computing system 5 of the fifth embodiment includes a computing apparatus 51 in place of the computing apparatus 11 of the first embodiment and a capability providing apparatus 52 in place of the capability providing apparatus 12.

As illustrated in FIG. 10, the computing apparatus 51 of the fifth embodiment includes, for example, a natural number storage 5101, a natural number selecting unit 5102, an input information providing unit 5104, a first computing unit 5105, a first power computing unit 1106, a first list storage 1107, a second computing unit 5108, a second list storage 5110, a determining unit 5111, a final output unit 1112, and a controller 1113.

As illustrated in FIG. 3, the capability providing apparatus 52 of the fifth embodiment includes, for example, a first output information computing unit 5201, a second output information computing unit 5202, a key storage 1204, and a controller 1205.

<Processes>

Processes of this embodiment will be described below. For the processes, let G and H be groups (for example commutative groups), f(x) be a decryption function for decrypting a ciphertext x, which is an element of the group H, with a particular decryption key s to obtain an element of the group G, generators of the groups G and H be μ_(g) and μ_(h), respectively, X₁ and X₂ be random variables having values in the group G, x₁ be a realization of the random variable X₁, and x₂ be a realization of the random variable X₂. It is assumed here that a plurality of natural numbers a are stored in the natural number storage 5101 of the computing apparatus 51.

As illustrated in FIG. 11, first, the natural number selecting unit 5102 of the computing apparatus 51 (FIG. 10) randomly reads one natural number a from among the plurality of natural numbers a stored in the natural number storage 5101. Information on the read natural number a is sent to the input information providing unit 5104 and the first power computing unit 1106 (step S5100).

The Controller 1113 Sets t=1 (Step S1102).

The input information providing unit 5104 generates and outputs first input information τ₁ and second input information τ₂ which are elements of the group H and each of which corresponds to an input ciphertext x. Preferably, the first input information τ₁ and the second input information τ₂ are information whose relation with the ciphertext x is scrambled. This enables the computing apparatus 51 to conceal the ciphertext x from the capability providing apparatus 52. Preferably, the second input information τ₂ of this embodiment further corresponds to the natural number a selected by the natural number selecting unit 5102. This enables the computing apparatus 51 to evaluate the decryption capability provided by the capability providing apparatus 52 with a high degree of accuracy (step S5103). A specific example of the pair of the first input information τ₁ and the second input information τ₂ is a pair of first input information τ₁ and the second input information τ₂ of any of the second to fourth embodiments when b=1.

As illustrated in FIG. 7, the first input information τ₁ is input in the first output information computing unit 5201 of the capability providing apparatus 52 (FIG. 3) and the second input information τ₂ is input in the second output information computing unit 5202 (step S5200).

The first output information computing unit 5201 uses the first input information τ₁ and the decryption key s stored in the key storage 1204 to correctly compute f(τ₁) with a probability greater than a certain probability and sets the result of the computation as first output information z₁ (step S5201). The second output information computing unit 5202 uses the second input information τ₂ and the decryption key s stored in the key storage 1204 to correctly compute f(τ₂) with a probability greater than a certain probability and sets the result of the computation as second output information z₂ (step S5202). That is, the first output information computing unit 5201 and the second output information computing unit 5202 output computation results that have an intentional or unintentional error. In other words, the result of the computation by the first output information computing unit 5201 may or may not be f(τ₁) and the result of the computation by the second output information computing unit 5202 may or may not be f(τ₂). A specific example of the pair of the first output information z₁ and the second output information z₂ is a pair of first output information z₁ and the second output information z₂ of any of the second to fourth embodiments when b=1.

The first output information computing unit 5201 outputs the first output information z₁ and the second output information computing unit 5202 outputs the second output information z₂ (step S5203).

Returning to FIG. 11, the first output information z₁ is input in the first computing unit 5105 of the computing apparatus 51 (FIG. 10) and the second output information z₂ is input in the second computing unit 5108. The first output information z₁ and the second output information z₂ are equivalent to the decryption capability provided by the capability providing apparatus 52 to the computing apparatus 51 (step S5104).

The first computing unit 5105 generates a computation result u=f(x)x₁ from the first output information z₁. A specific example of the computation result u is a result u of computation of any of the second to fourth embodiments when b=1. The result u of the computation is sent to the first power computing unit 1106 (step S5105).

The first power computing unit 1106 computes u′=u^(a). The pair of the result u of the computation and u′ computed on the basis of the result of the computation, (u, u′), is stored in the first list storage 1107 (step S1106).

The second computing unit 5108 generates a computation result v=f(x)^(a)x₂ from the second output information z₂. A specific example of the result v of the computation is a result v of the computation of any of the second to fourth embodiments. The result v of the computation is stored in the second list storage 5110 (step S5108).

The determining unit 5111 determines whether or not there is one that satisfies u′=v among the pairs (u, u′) stored in the first list storage 1107 and v stored in the second list storage 5110 (step S5110). If there is one that satisfies u′=v, the process proceeds to step S5114; if there is not one that satisfies u′=v, the process proceeds to step S1111.

At step S1111, the controller 1113 determines whether or not t=T_(max) (step S1111). Here, T_(max) is a predetermined natural number. If t=T_(max), the controller 1113 outputs information indicating that the computation is impossible, for example the symbol “⊥” (step S1113), then the process ends. If not t=T_(max), the controller 1113 increments t by 1, that is, sets t=t+1 (step S1112), then the process returns to step S5103.

At step S5114, the final output unit 1112 outputs u corresponding to u′ that has been determined to satisfy u′=v (step S5114). The obtained u is equivalent to u^(b′)v^(a′) when b=1 in the first to fourth embodiments. That is, u thus obtained can be a result f(x) of decryption of the ciphertext x with a particular decryption key s with a high probability. Therefore, the process described above is repeated multiple times and the value that has most frequently obtained among the values obtained at step S5114 can be chosen as the decryption result. As will be described later, u=f(x) can result with an overwhelming probability, depending on settings. In that case, the value obtained at step S5114 can be directly provided as a result of decryption.

<<Reason why Decryption Result f(x) can be Obtained>>

The reason why a decryption result f(x) can be obtained on the computing apparatus 51 of this embodiment will be described below. Terms required for the description will be defined first.

Black-Box:

A black-box F(τ) of f(τ) is a processing unit that takes an input of τϵH and outputs zϵG. In this embodiment, each of the first output information computing unit 5201 and the second output information computing unit 5202 is equivalent to the black box F(τ) for the decryption function f(τ). A black-box F(τ) that satisfies z=f(τ) for an element τϵ_(U)H arbitrarily selected from a group H and z=F(τ) with a probability greater than δ(0<δ≤1), that is, a black-box F(τ) for f(τ) that satisfies Pr[z=f(τ)|τϵ_(U) H,z=F(τ)]>δ  (1) is called a δ-reliable black-box F(τ) for f(τ). Here, δ is a positive value and is equivalent to the “certain probability” stated above.

Self-Corrector:

A self-corrector C^(F)(x) is a processing unit that takes an input of xϵH, performs computation by using a black-box F(τ) for f(τ), and outputs jϵG∪⊥. In this embodiment, the computing apparatus 51 is equivalent to the self-corrector C^(F)(x).

Almost Self-Corrector:

Assume that a self-corrector C^(F)(x) that takes an input of xϵH and uses a δ-reliable black-box F(τ) for f(τ) to perform computation outputs a correct value j=f(x) with a probability sufficiently greater than the provability with which the self-corrector C^(F)(x) outputs an incorrect value j≠f(x).

That is, assume that a self-corrector C^(F)(x) satisfies Pr[j=f(x)|j=C ^(F)(x),j≠⊥]>Pr[j≠f(x)|j=C ^(F)(x),j≠⊥]+Δ   (2) Here, Δ is a certain positive value (0<Δ<1). If this is the case, the self-corrector C^(F)(x) is called an almost self-corrector. For example, for a certain positive value Δ′(0<Δ′<1), if a self-corrector C^(F)(x) satisfies Pr[j=f(x)|j=C ^(F)(x)]>(⅓)+Δ′ Pr[j=⊥|j=C ^(F)(x)]<⅓ Pr[j≠f(x) and j≠⊥|j=C ^(F)(x)]<⅓, then the self-corrector C^(F)(x) is an almost self-corrector. Examples of Δ′ include Δ′= 1/12 and Δ′=⅓.

Robust Self-Corrector:

Assume that a self-corrector C^(F)(x) that takes an input of xϵH and uses a δ-reliable black-box F(τ) for f(τ) outputs a correct value j=f(x) or j=⊥ with an overwhelming probability. That is, assume that for a negligible error ξ (0≤ξ<1), a self-corrector C^(F)(x) satisfies Pr[j=f(x) or j=⊥|j=C ^(F)(x)]>1−ξ  (3) If this is the case, the self-corrector C^(F)(x) is called a robust self-corrector. An example of the negligible error ξ is a function vale ξ(k) of a security parameter k. An example of the function value ξ(k) is a function value ξ(k) such that {ξ(k)p(k)} converges to 0 for a sufficiently large k, where p(k) is an arbitrary polynomial. Specific examples of the function value ξ(k) include ξ(k)=2^(−k) and ξ(k)=2^(−√k).

A robust self-corrector can be constructed from an almost self-corrector. Specifically, a robust self-corrector can be constructed by executing an almost self-constructor multiple times for the same x and selecting the most frequently output value, except ⊥, as j. For example, an almost self-corrector is executed O(log(1/ξ)) times for the same x and the value most frequently output is selected as j to construct robust self-corrector. Here, O(⋅) represents O notation.

Pseudo-Free Action:

An upper bound of the probability Pr[α ^(a)=β and α≠e _(g) |aϵ _(u)Ω,αϵX₁ ,βϵX ₂]  (4) of satisfying α^(a)=β for all possible X₁ and X₂ is called a pseudo-free indicator of a pair (G, Ω) and is represented as P(G, Ω), where G is a group, Ω is a set of natural numbers Ω={0, . . . , M} (M is a natural number greater than or equal to 1), α and β are realizations αϵX₁ (α≠e_(g)) and βϵX₂ of random variables X₁ and X₂ that have values in the group G, and aϵΩ. If a certain negligible function ζ(k) exists and P(G,Ω)<ζ(k)  (5), then a computation defined by the pair (G, Ω) is called a pseudo-free action. Note that a computation defined by a group is expressed multiplicatively in the fifth embodiment. That is, “α^(a)” for αϵG means that a computation defined at the group G is applied a times to α. An example of the negligible function ζ(k) is such that {ζ(k)p(k)} converges to 0 for a sufficiently large k, where p(k) is an arbitrary polynomial. Specific examples of the function ζ(k) include ζ(k)=2^(−k) and ζ(k)=2^(−√k). For example, if the probability of Formula (4) is less than O(2^(−k)) for a security parameter k, a computation defined by the pair (G, Ω) is a pseudo-free action. For example, if the number of the elements |Ω·α| of a set Ω·α={a(α)|aϵΩ} exceeds 2^(k) for any αϵG where α≠e_(g), a computation defined by the pair (G, Ω) can be a pseudo-free action. There are many such examples. For example, if the group G is a residue group Z/pZ modulo prime p, the prime p is the order of 2^(k), the set Ω={0, . . . , p−2}, a(α) is α^(a) ϵZ/pZ, and α≠e_(g), then Ω·α={α^(a)|a=0, . . . , p−2}={e_(g), α¹, . . . , α^(p-2)} and |Ω·α|=p−1. If a certain constant C exists and k is sufficiently large, |Ω·α|>C2^(k) is satisfied because the prime p is the order of 2^(k). Here, the probability of Formula (4) is less than C⁻¹2^(−k) and a computation defined by such pair (G, Ω) is a pseudo-free action.

δ^(γ)-Reliable Randomizable Sampler:

A randomizable sampler that whenever a natural number a is given, uses the δ-reliable black-box F(τ) for f(τ) and returns w^(a)x′ corresponding to a sample x′ that depends on a random number X for wϵG and in which the probability that w^(a)x′=w^(a) is greater than δ^(γ) (γ is a positive constant), that is, Pr[w ^(a) x′=w ^(a)]>δ^(γ)  (6) is satisfied, is called a δ^(γ)-reliable randomizable sampler. The combination of the input information providing unit 5104, the second output information computing unit 5202, and the second computing unit 5108 of this embodiment is a δ^(γ)-reliable randomizable sampler for w=f(x).

The definitions given above will be used to describe the reason why a decryption result f(x) can be obtained by using the computing apparatus 51 of this embodiment.

At step S5110 of this embodiment, determination is made as to whether u′=v, that is, whether u^(a)=v. Since the combination of the input information providing unit 5104, the second output information computing unit 5202, and the second computing unit 5108 of this embodiment is a δ^(γ)-reliable randomizable sampler (Formula (6)), u^(a)=v holds (Yes at step S5110) with an asymptotically large probability if T_(max) is greater than a certain value determined by k, δ and γ. For example, Markov's inequality shows that if T_(max)≥4/δ^(γ), the probability that u^(a)=v holds (Yes at step S5110) is greater than ½.

Since u=f(x)x₁ and v=f(x)^(a)x₂ in this embodiment, x₁ ^(a)=x₂ holds if u^(a)=v holds. x₁ ^(a)=x₂ holds if x₁=x₂=e_(g) or x₁≠e_(g). If x₁=x₂=e_(g), then u=f(x) and therefore u output at step S5114 is a correct decryption result f(x). On the other hand, if x₁≠e_(g), then u≠f(x) and therefore u output at step S5114 is not a correct decryption result f(x).

If a computation defined by a pair (G, Ω) of a group G and a set Q to which a natural number a belongs is a pseudo-free action or τ_(max) ²P(G, Ω) is asymptotically small for a pseudo-free index P(G, Ω), the probability that x₁≠e_(g) (Formula (4)) when u^(a)=v is asymptotically small. Accordingly, the probability that x₁=e_(g) when u^(a)=v is asymptotically large. Therefore, if a computation defined by a pair (G, Ω) is a pseudo-free action or τ_(max) ²P(G, Ω) is asymptotically small, the probability that an incorrect decryption result f(x) is output when u^(a)=v is sufficiently smaller than the probability that a correct decryption result f(x) is output when u^(a)=v. In this case, it can be said that the computing apparatus 51 is an almost self-corrector (see Formula (2)). Therefore, a robust self-corrector can be constructed from the computing apparatus 51 as described above and a correct decryption result f(x) can be obtained with an overwhelming probability. If a computation defined by (G, Ω) is a pseudo-free action, the probability that an incorrect decryption result f(x) is output when u^(a)=v is also negligible. In that case, the computing apparatus 51 outputs a correct decryption result f(x) or ⊥ with an overwhelming probability.

Note that “η(k′) is asymptotically small” means that k₀ is determined for an arbitrary constant ρ and the function value η(k′) for any k′ that satisfies k₀<k′ for k₀ is less than ρ. An example of k′ is a security parameter k.

“η(k) is asymptotically large” means that k₀ is determined for an arbitrary constant ρ and the function value 1−η(k′) for any k′ that satisfies k₀<k′ for k₀ is less than ρ.

<<δ^(γ)-Reliable Randomizable Sampler and Security>>

Consider the following attack.

A black-box F(τ) or a part of the black-box F(τ) intentionally outputs an invalid z or a value output from the black-box F(τ) is changed to an invalid z.

w^(a)x′ corresponding to the invalid z is output from the randomizable sampler.

w^(a)x′ corresponding to the invalid z increases the probability with which the self-corrector C^(F)(x) outputs an incorrect value even though u^(a)=v holds (Yes at step S5110) in the self-corrector C^(F)(x).

This attack is possible if the probability distribution D_(a)=w^(a)x′w^(−a) of an error of w^(a)x′ output from the randomizable sampler for a given natural number a depends on the natural number a. For example, if tampering is made so that v output from the second computing unit 5108 is f(x)^(a)x₁ ^(a), then u^(a)=v always holds regardless of the value of x₁. Therefore, it is desirable that the probability distribution D_(a)=w^(a)x′w^(−a) of an error of w^(a)x′ output from the randomizable sampler for a given natural number a do not depend on the natural number a.

Alternatively, it is desirable that the randomizable sampler be such that a probability distribution D that has a value in a group G that cannot be distinguished from the probability distribution D_(a)=w^(a)x′w^(−a) of an error of w^(a)x′ for any element aϵ^(∀)Ω of a set Ω exists (the probability distribution D_(a) and the probability distribution D are statistically close to each other). Note that the probability distribution D does not depend on a natural number a. That the probability distribution D_(a) and the probability distribution D cannot be distinguished from each other means that the probability distribution D_(a) and the probability distribution D cannot be distinguished from each other by a polynomial time algorithm. For example, if Σ_(gϵG) |Pr[gϵD]−Pr[gϵD _(a)]|<ζ  (7) is satisfied for negligible ζ(0≤ζ1), the probability distribution D_(a) and the probability distribution D cannot be distinguished from each other by the polynomial time algorithm. An example of negligible ζ is a function value ζ(k) of the security parameter k. An example of the function value ζ(k) is a function value such that {ζ(k)p(k)} converges to 0 for a sufficiently large k, where p(k) is an arbitrary polynomial. Specific examples of the function ζ(k) include ζ(k)=2^(−k) and ζ(k)=2^(−√k). These facts also apply to the first to fourth embodiments which use natural numbers a and b.

Sixth Embodiment

A sixth embodiment is a mode in which the present invention is applied to decryption of the GHV encryption scheme (see Reference literature 1 “C, Genrty, S. Halevi and V. Vaikuntanathan, ‘A Simple BGNType Cryptosystem from LWE,’ Advances in Cryptology—EUROCRYPT 2010, LNCS 6110, pp. 506-522, Springer-Verlag, 2010”, for example), which is a type of lattice-based cryptography. The following description will focus on differences from the embodiments described above.

<Configuration>

As illustrated in FIG. 1, a proxy computing system 6 of the sixth embodiment includes a computing apparatus 61 in place of the computing apparatus 11 of the first embodiment and a capability providing apparatus 62 in place of the capability providing apparatus 12.

As illustrated in FIG. 12, the computing apparatus 61 of the sixth embodiment includes, for example, a matrix storage 6101, a matrix selecting unit 6102, an input information providing unit 6104, a first computing unit 6105, a matrix product computing unit 6106, a first list storage 6107, a second computing unit 6108, a second list storage 6110, a determining unit 6111, a final output unit 6112, and a controller 1113.

As illustrated in FIG. 14, the input information providing unit 6104 of this embodiment includes, for example, a first random matrix selecting unit 6104 a, a second random matrix selecting unit 6104 b, a first encryption unit 6104 c, a second encryption unit 6104 d, a first input information computing unit 6104 e, a third random matrix selecting unit 6104 f, a fourth random matrix selecting unit 6104 g, a third encryption unit 6104 h, a fourth encryption unit 6104 i, and a second input information computing unit 6104 j.

As illustrated in FIG. 13, the capability providing apparatus 62 of the sixth embodiment includes, for example, a first output information computing unit 6201, a second output information computing unit 6202, a key storage 6204, and a controller 1205.

<Processes>

Processes of this embodiment will be described below. In this embodiment, let G_(M) be a set of ι×ι matrices, H_(M) be a set of ι×ι matrices, _(M)X₁ and _(M)X₂ be random variables having values in the set G_(M), _(M)x₁ be a realization of the random variable _(M)X₁, _(M)x₂ be a realization of the random variable _(M)X₂, and a_(M) be an element of the set H_(M). In this embodiment, PK be a ι×κ matrix which is an encryption key (public key), SK be a decryption key (secret key) which is a ι×ι matrix that satisfies PK·SK=0, CM be a κ×ι matrix, NM be a ι×ι matrix, UM be a ι×ι unit matrix, PT be a plaintext PTϵG_(M) which is an element of the set G_(M), x_(M) be a ciphertext x_(M)ϵH_(M) which is an element of the set H_(M), ENC_(M) be an encryption function for encrypting the plaintext PT which is an element of the set G_(M) to obtain the ciphertext x_(M)ϵH_(M), and f_(M)(x_(M)) be a decryption function for decrypting the ciphertext x_(M)ϵH_(M) with a particular decryption key SK to obtain the plaintext PT which is an element of the set G_(M). The decryption function f_(M)(x_(M)) is a homomorphic function. For example, let G_(M) be a set of ι×ι matrices (Z/2Z)^(ι×ι), H_(M) be a set of ι×ι matrices (Z/qZ)^(ι×ι), the encryption key PK be a ι×κ matrix (Z/qZ)^(ι×κ), the decryption key SK be a ι×ι matrix (Z/qZ)^(ι×ι), CM be a randomly selected κ×ι matrix (Z/qZ)^(κ×ι), NM be a ι×ι matrix (Z/qZ)^(ι×ι) according to a Gaussian distribution, UM be a ι×ι unit matrix (Z/2Z)^(ι×ι), the encryption function ENC_(M) (PT) be PK·CM+2·NM+PT(mod q), and the decryption function f_(M)(x_(M)) be SK⁻¹{SK·x_(M)·SK^(T)(mod q)}(SK^(T))⁻¹(mod 2). Here, κ, ι and q are positive integers, ⋅^(T) is the transposed matrix of ⋅, and (Z/qZ)^(κ×ι) is a matrix of κ rows and ι columns having members of a factor ring Z/qZ modulo q as elements. In the sixth embodiment, the product between matrices α₁ and α₂ is written as α₁·α₂ and the sum of matrices α₁ and α₂ is written as α₁+α₂. A matrix that is equal to the each element of a matrix α by a natural number β is written as β·α.

It is assumed in this embodiment that a plurality of matrices a_(M)ϵH_(M) are stored in the matrix storage 6101 of the computing apparatus 61 (FIG. 12) and a decryption key SK is stored in the key storage 6204 of the capability providing apparatus 62 (FIG. 13) in a secure manner. As illustrated in FIG. 15, first, the matrix selecting unit 6102 of the computing apparatus 61 (FIG. 12) uniformly randomly selects and reads one matrix a_(M) from among the plurality of matrices stored in the matrix storage 6101. Information of the read matrix a_(M) is sent to the input information providing unit 6104 and the matrix product computing unit 6106 (step S6100).

The Controller 1113 Sets t=1 (Step S1102).

The input information providing unit 6104 generates and outputs first input information _(M)τ₁ and second input information _(M)τ₂ which are elements of the set H_(M) and each of which corresponds to an input ciphertext x_(M). Preferably, the first input information _(M)τ₁ and the second input information _(M)τ₂ are information whose relation with the ciphertext x_(M) is scrambled. This enables the computing apparatus 61 to conceal the ciphertext x_(M) from the capability providing apparatus 62. The second input information _(M)τ₂ further corresponds to an element a_(M). This enables the computing apparatus 61 to evaluate the decryption capability provided by the capability providing apparatus 62 with a high degree of accuracy (step S6103). A specific example of step S6103 will be described below with reference to FIG. 16.

[Specific Example of Step S6103]

The first random matrix selecting unit 6104 a of the input information providing unit 6104 (FIG. 14) uniformly randomly selects an element M_(R1) of the set G_(M) (step 6103 a). The selected M_(R1) is sent to the first encryption unit 6104 c and the first computing unit 6105 (step S6103 a). The second random matrix selecting unit 6104 b selects uniform and random matrices CM₁₁ and CM₁₂ϵ(Z/qZ)^(κ×ι) of κ×ι. The selected CM₁₁ and CM₁₂ are sent to the first input information computing unit 6104 e (step S6103 b). The first encryption unit 6104 c uses the public key PK to generate a first ciphertext C_(R1)=PK·CM+2·NM+M_(R1) (mod q) which is a ciphertext ENC_(M)(M_(R1)) of M_(R1). The first ciphertext C_(R1) is sent to the first input information computing unit 6104 e (step S6103 c). The second encryption unit 6104 d uses the public key PK to generate a second ciphertext C_(UM)=PK·CM+2·NM+UM (mod q) which is a ciphertext ENC_(M)(UM) of the unit matrix UM. The second ciphertext C_(UM) is sent to the first input information computing unit 6104 e (step S6103 d). The first input information computing unit 6104 e further takes an input of the ciphertext x_(M). The first input information computing unit 6104 e obtains and outputs (x_(M)·C_(UM)+C_(R1))+PK·CM₁₁+2·NM+CM₁₂ ^(T)·PK^(T) as first input information _(M)τ₁. Note that the order of the products of the matrices is not particularly specified. That is, the first input information computing unit 6104 e may compute Re(C_(x))=C_(x)+PK·CM₁₁+2·NM+CM₁₂ ^(T)·PK^(T) where C_(x)=x_(M)·C_(UM)+C_(R1), to generate the first input information _(M)τ₁ or may compute Re(C_(x)) where C_(x)=C_(UM) x_(M)+C_(R1) to generate the first input information _(M)τ₁ (step S6103 e).

The third random matrix selecting unit 6104 f uniformly randomly selects an element M_(R2) of the set G_(M). The selected M_(R2) is sent to the third encryption unit 6104 h and the second computing unit 6108 (step S6103 f). The fourth random matrix selecting unit 6104 g selects random matrices CM₂₁ and CM₂₂ϵ(Z/qZ)^(κ×ι) of κ×ι. The selected CM₂₁ and CM₂₂ are sent to the second input information computing unit 6104 j (step S6103 g). The third encryption unit 6104 h uses the public key PK to generate a third ciphertext C_(R2)=PK·CM+2·NM+M_(R2)(mod q) which is a ciphertext ENC_(M)(M_(R2)) of M_(R2). The third ciphertext C_(R2) is sent to the second input information computing unit 6104 j (step S6103 h). Matrix a_(M) is input in the fourth encryption unit 6104 i. The fourth encryption unit 6104 i uses the public key PK to generate a fourth ciphertext C_(a)=PK·CM+2·NM+a_(M)(mod q) which is a ciphertext ENC_(M)(a_(M)) of the matrix a_(M). The fourth ciphertext C_(a) is sent to the second input information computing unit 6104 j (step S6103 i). The second input information computing unit 6104 j further takes an input of the ciphertext x_(M). The second input information computing unit 6104 j obtains and outputs (x_(M)·C_(a)+C_(R2))+PK·CM₂₁+2·NM+CM₂₂ ^(T)·PK^(T) as second input information _(M)τ₂. The second input information computing unit 6104 j may compute Re(C_(x)) where C_(x)=x_(M)·C_(a)+C_(R2) to generate the second input information _(M)τ₂ or may compute Re(C_(x)) where C_(x)=C_(a) x_(M)+C_(R2) to generate the second input information _(M)τ₂ ((step S6103 j)/end of description of [Specific example of step S6103]).

As illustrated in FIG. 17, the first input information _(M)τ₁ is input in the first output information computing unit 6201 of the capability providing apparatus 62 (FIG. 13) and the second input information _(M)τ₂ is input in the second output information computing unit 6202 (step S6200).

The first output information computing unit 6201 uses the first input information _(M)τ₁ and the decryption key SK stored in the key storage 6204 to correctly compute f_(M)(_(M)τ₁)=SK⁻¹ {SK·_(M)τ₁·SK^(T)(mod q)}(SK^(T))⁻¹(mod 2) with a probability greater than a certain probability and sets the obtained result of the computation as first output information _(M)z₁ (step S6201). The second output information computing unit 6202 uses the second input information _(M)τ₂ and the decryption key SK stored in the key storage 6204 to correctly compute f_(M)(_(M)τ₂)=SK⁻¹{SK·_(M)τ₂·SK^(T)(mod q)}(SK^(T))⁻¹(mod 2) with a probability greater than a certain probability and sets the obtained result of the computation as second output information _(M)z₂ (step S6202). That is, the first output information computing unit 6201 and the second output information computing unit 6202 outputs computation results that have an intentional or unintentional error. In other words, the result of the computation by the first output information computing unit 6201 may or may not be f_(M)(_(M)τ₁) and the result of the computation by the second output information computing unit 6202 may or may not be f_(M)(_(M)τ₂).

The first output information computing unit 6201 outputs the first output information _(M)z₁ and the second output information computing unit 6202 outputs the second output information _(M)z₂ (step S6203).

Returning to FIG. 15, the first output information _(M)z₁ is input in the first computing unit 6105 of the computing apparatus 61 (FIG. 12) and the second output information _(M)z₂ is input in the second computing unit 6108. The first output information _(M)z₁ and the second output information _(M)z₂ are equivalent to the decryption capability provided by the capability providing apparatus 62 to the computing apparatus 61 (step S6104).

The first computing unit 5105 uses the first output information _(M)z₁ to compute _(M)z₁−M_(R1) and sets the result of the computation as u_(M). The result u_(M) of the computation is sent to the matrix product computing unit 6106. Here, u_(M)=_(M)z₁−M_(R1)=f_(M)(x_(M))+_(M)X₁. That is, u_(M) serves as a sampler having an error _(M)X₁ for f_(M)(x_(M)). The reason will be described later (step S6105).

The matrix product computing unit 6106 obtains u_(M)′=u_(M)·a_(M). Note that the matrix product computing unit 6106 may compute u_(M)·a_(M) to obtain u_(M)′ or may compute a_(M)·u_(M) to obtain u_(M)′. The pair (u_(M), u_(M)′) of the result u_(M) of the computation and u_(M)′ computed on the basis of the result of the computation is stored in the first list storage 6107 (step S6106).

The second computing unit 6108 uses the second output information _(M)z₂ to compute _(M)z₂−M_(R2) and sets the result of the computation as v_(M). The result v_(M) of the computation is stored in the second list storage 6110. Here, v_(M)=_(M)z₂−M_(R2)=f_(M)(x_(M))·a_(M)+_(M)X₂. That is, v_(M) is an output of a randomizable sampler having an error _(M)X₂ for f_(M)(x_(M)). The reason will be described later (step S6108).

The determining unit 6111 determines whether or not there is one that satisfies u_(M)′=v_(M) among the pairs (u_(M), u_(M)′) stored in the first list storage 6107 and v_(M) stored in the second list storage 6110 (step S6110). If there is one that satisfies u_(M)′=V_(M), the process proceeds to step S6114; if there is not one that satisfies u_(M)′=V_(M), the process proceeds to step S1111.

At step S1111, the controller 1113 determines whether or not t=T_(max) (step S1111). Here, T_(max) is a predetermined natural number. If t=T_(max), the controller 1113 outputs information indicating that the computation is impossible, for example, the symbol “⊥” (step S1113), then the process ends. If not t=T_(max), the controller 1113 increments t by 1, that is, sets t=t+1 (step S1112), then the process returns to step S6103.

At step S6114, the final output unit 6112 outputs u_(M) corresponding to u_(M)′ that has been determined to satisfy u_(M)′=v_(M) (step S6114). The u_(M) thus obtained can be a decryption result f_(M)(x_(M)) resulting from decrypting the ciphertext x_(M) with the decryption key SK with a high probability (the reason will be described later). Therefore, the process described above is repeated multiple times and the value most frequently obtained among the values obtained at step S6114 can be chosen as the decryption result. Depending on settings, u_(M)=f_(M)(x_(M)) can result with an overwhelming probability. In that case, the value obtained at step S6114 can be directly provided as the result of decryption.

<<Reason why _(M)z₁−M_(r1) and _(M)z₂−M_(r2) are Outputs of a Sampler and Randomizable Sampler that have Errors _(M)X₁ and _(M)X₂, Respectively, for f_(M)(x_(M))>>

Because of the homomorphy of f_(M)(x_(M)), f_(M)(x_(M) C_(a)+C_(R2))=f_(M)(x_(M))·f_(M)(C_(a))+f_(M)(C_(R2))=f_(M)(x_(M))·a_(M)+M_(R2) is satisfied, f_(M)(x_(M))·a_(M)=f_(M)(x_(M) C_(a)+C_(R2))−M_(R2)=f_(M)(_(M)τ₂)−M_(R2) is satisfied, and M_(R2)=f_(M)(_(M)τ₂)−f_(M)(x_(M))·a_(M) is satisfied. Therefore, letting _(M)z₂=F_(M)(_(M)τ₂), then _(M)z₂−M_(R2)=F_(M)(_(M)τ₂)−f_(M)(_(M)τ₂)+f_(M)(x_(M))·a_(M)=f_(M)(x_(M))·a_(M)+{F_(M)(_(M)τ₂)−f_(M)(_(M)τ₂)} is satisfied. Because of the uniform randomness of CM₂₁, CM₂₂ and M_(R2) corresponding to _(M)τ₂, _(M)z₂−M_(R2) is statistically close to f_(M)(x_(M))·a_(M)+_(M)x₂. Here, _(M)x₂ is a realization of the random variable _(M)X₂=F_(M)(ENC_(M)(_(M)U₂))−_(M)U₂ (_(M)U₂ uniformly randomly distributes on G_(M)). Therefore, _(M)z₂−M_(R2) is an output of a randomizable sampler having an error _(M)X₂ for f_(M)(x_(M)).

Likewise, f_(M)(x_(M)·C_(UM)+C_(R1))=f_(M)(x_(M)) f_(M)(C_(UM)) f_(M)(C_(R1))=f_(M)(x_(M))·UM+M_(R1) is satisfied, f_(M)(x_(M))=f_(M)(x_(M) C_(UM)+C_(R1))−M_(R1)=f_(M)(_(M)τ₁)−M_(R1) is satisfied, and M_(R1)=f_(M)(_(M)τ₁)−f_(M)(x_(M)) is satisfied. Therefore, letting _(M)z₁=F_(M)(_(M)τ₁), then _(M)z₁−M_(R1)=F_(M)(_(M)τ₁) f_(M)(_(M)τ₁) f_(M)(x_(M))=f_(M)(x_(M))+{F_(M)(_(M)τ₁)−f_(M)(_(M)τ₁)} is satisfied. Because of the uniform randomness of CM₁₁, CM₁₂ and M_(R1) corresponding to _(M)τ₁, _(M)z₁−M_(R1) is statistically close to f_(M)(x_(M))+_(M)x₁. Here, _(M)x₁ is a realization of the random variable _(M)X₁=F_(M)(ENC_(M)(_(M)U₁))−_(M)U₁ (_(M)U₁ uniformly randomly distributes on G_(M)). Therefore, the above-described configuration which outputs _(M)z₁−M_(R1) serves as a sampler having an error _(M)X₁ for f_(M)(x_(M)).

<<Reason why Decryption Result f_(M)(x_(M)) can be Obtained>>

For the same reason described in the section <<Reason why decryption result f(x) can be obtained>> in the fifth embodiment, a correct decryption result f_(M)(x_(M)) can be obtained in the sixth embodiment as well. However, since the sixth embodiment deals with matrices, G and H in the section <<Reason why decryption result f(x) can be obtained>> in the fifth embodiment are replaced with G_(M) and H_(M), f(x) is replaced with f_(M)(x_(M)), τ is replaced with _(M)τ, F(τ) is replaced with F_(M)(_(M)τ), z is replaced with _(M)z, x is replaced with x_(M), X₁ and X₂ are replaced with _(M)X₁ and _(M)X₂, x₁ and x₂ are replaced with _(M)x₁ and _(M)x₂, e_(g) is replaced with a unit matrix _(M)e_(g) of ι×ι, and multiplicative expressions are replaced with additive expressions (for example α^(β)γ is replaced with α·β+γ). Furthermore, “pseudo-free action” in the sixth embodiment is defined as follows.

Pseudo-Free Action:

An upper bound of the probability Pr[α _(M) ·a _(M) and α_(M)≠_(M) e _(g) |a _(M)ϵ_(U)Ω_(M),α_(M)ϵ_(M) X ₁,β_(M)ϵ_(M) X ₂] of satisfying α_(M)·a_(M)=β_(M) for all possible _(M)X₁ and _(M)X₂ is called a pseudo-free indicator of a pair (G_(M), Ω_(M)) and is represented as P(G_(M), Ω_(M)), where G_(M) is a matrix, Ω_(M) is a set of matrices Ω_(M)={0_(M), . . . , M_(M)}, α_(M) and β_(M) are realizations α_(M)ϵ_(M)X₁ (α_(M)≠_(M)e_(g)) and β_(M)ϵ_(M)X₂ of random variables _(M)X₁ and _(M)X₂ on G_(M), and a_(M)ϵΩ_(M). If a certain negligible function ζ(k) exists and P(G _(M),Ω_(M))<ζ(k) then a computation defined by the pair (G_(M), Ω_(M)) is called a pseudo-free action.

[Variations of First to Sixth Embodiments]

As has been described above, the capability providing apparatus provides first output information z₁ and second output information z₂ to the computing apparatus without providing a decryption key and the computing apparatus outputs u^(b′)v^(a′) in the embodiments described above. The probability of U^(b′)V^(a′) being the decryption value of the ciphertext x is high. Thus, the capability providing apparatus can provide the decryption capability to the computing apparatus without providing a decryption key.

Note that the present invention is not limited to the embodiments described above. For example, the random variables X₁, X₂ and X₃ may be the same or different. Similarly, the random variables _(M)X₁ and _(M)X₂ may be the same or different.

Each of the first random number generator, the second random number generator, the third random number generator, the fourth random number generator, the fifth random number generator, the sixth random number generator and the seventh random number generator generates uniform random numbers to achieve the highest security of the proxy computing system. However, if the level of security required is not so high, at least some of the first random number generator, the second random number generator, the third random number generator, the fourth random number generator, the fifth random number generator, the sixth random number generator and the seventh random number generator may generate random numbers that are not uniform random numbers. Similarly, a non-uniform random matrix may be selected in the sixth embodiment instead of uniformly randomly selecting a matrix. While it is desirable from the computational efficiency point of view that random numbers which are natural numbers greater than or equal to 0 and less than K_(H) or random numbers that are natural numbers greater than or equal to 0 and less than K_(G) be selected as in the embodiments described above, random numbers that are natural numbers greater than or equal to K_(H) or K_(G) may be selected instead.

The process of the capability providing apparatus may be performed multiple times each time the computing apparatus provides first input information τ₁ and second input information τ₂ which are elements of a group H and correspond to the same a and b to the capability providing apparatus. This enables the computing apparatus to obtain a plurality of pieces of first output information z₁, second output information z₂, and third output information z₃ each time the computing apparatus provides first input information τ₁ and the second input information τ₂ to the capability providing apparatus. Consequently, the number of exchanges and the amount of communication between the computing apparatus and the capability providing apparatus can be reduced. The same applies to the first input information _(M)τ₁ and the second input information _(M)τ₂ of the sixth embodiment.

The computing apparatus may provide a plurality of pieces of first input information τ₁ and second input information τ₂ to the capability providing apparatus at once and may obtain a plurality of pieces of corresponding first output information z₁, second output information z₂ and third output information z₃ at once. This can reduce the number of exchanges between the computing apparatus and the capability providing apparatus. The same applies to the first input information _(M)τ₁ and the second input information _(M)τ₂ of the sixth embodiment.

Check may be made to see whether u and v obtained at the first computing unit and the second computing unit of any of the first to fifth embodiments are elements of the group G. If u and v are elements of the group G, the process described above may be continued; if u or v is not an element of the group G, information indicating that the computation is impossible, for example, the symbol “⊥” may be output. Similarly, check may be made to see whether u_(M) and v_(M) obtained at the first computing unit and the second computing unit of the sixth embodiment is an element of G_(M). If u_(M) and v_(M) are elements of G_(M), the process described above may be continued; if u_(M) or G_(M) is not an element of G_(M), information indicating that the computation is impossible, for example, the symbol “⊥” may be output.

The units of the computing apparatus may exchange data among them directly or through a memory, which is not depicted. Similarly, the units of the capability providing apparatus may exchange data among them directly or through a memory, which is not depicted.

Furthermore, the processes described above may be performed in time sequence as described, or may be performed in parallel with one another or individually, depending on the throughput of the apparatuses that performs the processes or as needed. It would be understood that other modifications can be made without departing from the spirit of the present invention.

Seventh Embodiment

A seventh embodiment of the present invention will be described.

<Configuration>

As illustrated in FIG. 18, a proxy computing system 101 of the seventh embodiment includes, for example, a computing apparatus 111 that not have a decryption key, capability providing apparatuses 112-1, . . . , 112-Γ (Γ is an integer greater than or equal to 2) that has decryption keys s₁, . . . , s_(Γ), respectively, and a decryption control apparatus 113 that controls a decryption capability of the computing apparatus 111. The decryption control apparatus 113 controls a decryption capability provided by the capability providing apparatuses 112-1, . . . , 112-Γ to the computing apparatus 111 and the computing apparatus 111 uses the decryption capability provided by the capability providing apparatus 112-1, . . . , 112-Γ to decrypt a ciphertext. The computing apparatus 111, the capability providing apparatuses 112-1, . . . , 112-Γ, and the decryption control apparatus 113 are configured so that information can be exchanged between them. For example, the computing apparatus 111, the probability providing apparatuses 112-1, . . . , 112-Γ, and the decryption control apparatus 113 are capable of exchange information through a transmission line, a network, a portable recording medium, and/or other medium.

As illustrated in FIG. 19, the computing apparatus 111 of the seventh embodiment includes, for example, a natural number storage 11101, a natural number selecting unit 11102, an integer computing unit 11103, an input information providing unit 11104, a first computing unit 11105, a first power computing unit 11106, a first list storage 11107, a second computing unit 11108, a second power computing unit 11109, a second list storage 11110, a determining unit 11111, a final output unit 11112, a recovering unit 11100, and a controller 11113. Examples of the computing apparatus 111 include a device having a computing function and a memory function, such as a card reader-writer apparatus and a mobile phone, and a well-known or specialized computer that includes a CPU (central processing unit) and a RAM (random-access memory) in which a special program is loaded.

As illustrated in FIG. 20, the capability providing apparatus 112-ι (ι=1, . . . ω, where ω is an integer greater than or equal to 2 and less than or equal to Γ) of this embodiment includes, for example, a first output information computing unit 11201-ι, a second output information computing unit 11202-ι, a key storage 11204-ι, and a controller 11205-ι. Examples of the capability providing apparatus 112-ι include a tamper-resistant module such as an IC card and an IC chip, a device having computing and memory functions, such as a mobile phone, and a well-known or specialized computer including a CPU and a RAM in which a special program is loaded. As will be described later, the capability providing apparatuses 112-1, . . . , 112-ω are selected from the capability providing apparatuses 112-1, . . . , 112-Γ. If there are capability providing apparatuses 112-(ι+1), . . . , 112-Γ, the capability providing apparatuses 112-(ι+1), . . . , 112-Γ have the same configuration as the capability providing apparatus 112-ι.

As illustrated in FIG. 21, the decryption control apparatus 113 of the seventh embodiment includes, for example, an ciphertext storage 11301, a control instruction unit 11302, an output unit 11303, a controller 11304, a key storage 11305, and an encryption unit 11306. Examples of the decryption control apparatus 113 include a device having computing and memory functions, such as a mobile phone, and a well-known or specialized computer that includes a CPU and a RAM in which a special program is loaded.

<Processes>

Processes of this embodiment will be described below. For the processes, let G, and H, be groups (for example commutative groups), ω be an integer greater than or equal to 2, ι=1, . . . , ω, f_(ι)(λ_(ι)) be a decryption function for decrypting a ciphertext λ_(ι) which is an element of the group H_(ι) with a particular decryption key s_(ι) to obtain an element of the group G_(ι), generators of the groups G_(ι) and H_(ι) be μ_(ι,g) and μ_(ι,h), respectively, X_(ι,1) and X_(ι,2) be random variables having values in the group G_(ι), x_(ι,1) be a realization of the random variable X_(ι,1), and x_(ι,2) be a realization of the random variable X_(ι,2). Note that ω in this embodiment is a constant. It is assumed here that a plurality of pairs of natural numbers a(ι) and b(ι) that are relatively prime to each other (a(ι), b(ι)) are stored in the natural number storage 11101 of the computing apparatus 111. The term “natural number” means an integer greater than or equal to 0. Let I_(ι) be a set of pairs of relatively prime natural numbers that are less than the order of the group G_(ι), then it can be considered that pairs (a(ι), b(ι)) of natural numbers a(ι) and b(ι) corresponding to a subset S_(ι) of I_(ι) are stored in the natural number storage 11101. It is also assumed that a particular decryption key sι is stored in the key storage 12104 of the capability providing apparatus 112-ι in a secure manner. It is assumed that encryption keys pk₁, . . . pk_(Γ) corresponding to the decryption keys s₁, . . . , s_(Γ), respectively, are stored in the key storage 11305 of the decryption control apparatus 113. Examples of the decryption keys s_(ι) and encryption keys pk_(ι) are secret keys and public keys of public key cryptography. Processes of the computing apparatus 111 are performed under the control of the controller 11113, processes of the capability providing apparatus 112-ι are performed under the control of the controller 11205-ι, and processes of the decryption control apparatus 113 are performed under the control of the controller 11304.

<Encryption Process>

As illustrated in FIG. 24, first, a message mes is input in the encryption unit 11306 of the decryption control apparatus 113 (FIG. 21). The encryption unit 11306 randomly selects ω encryption keys pk₁, . . . pk_(ω) from the encryption keys pk₁, . . . pk_(Γ) (step S11301). The encryption unit 11306 generates ω shares sha₁, sha_(ω) from the message mes(step S11302). A method for generating the shares sha₁, . . . sha_(ω) will be described below.

Example 1 of Shares

Shares sha₁, sha_(ω) are generated so that a bit combination value sha₁| . . . |sha_(ω) of the ω shares sha₁, . . . , sha_(ω) is the message mes.

Example 2 of Shars

Shares sha₁, . . . sha_(ω) are generated so that the exclusive OR of the ω shares sha₁, . . . shar_(ω) is the message mes.

Example 3 of Shars

The message mes is secret-shared by a secret sharing scheme such as Shamir's secret sharing to generate shares sha₁, . . . sha_(ω) (End of description of examples of the method for generating shares).

Then, the encryption unit 11306 encrypts the share sha_(ι) with an encryption key pk_(ι) to generate a ciphertext λ_(ι) for each of ι=1, . . . , ω. The generated ciphertexts λ₁, . . . , λ_(ω) are stored in the ciphertext storage 11301 (step S11303).

Then, the ciphertexts λ₁, . . . , λ_(ω) stored in the ciphertext storage 11301 are output from the output unit 11301 and input in the computing apparatus 111 (FIG. 19) (step S11304). The ciphertexts λ₁, . . . , λ_(ω) may or may not be sent at a time.

<Decryption Process>

A decryption process for decrypting a ciphertext according to this embodiment will be described with reference to FIG. 25. The process described below is performed for each of ι=1, . . . , ω.

First, the natural number selecting unit 11102 of the computing apparatus 111 (FIG. 19) randomly reads one pair (a(ι), b(ι)) of natural numbers from among a plurality of pairs of natural numbers (a(ι), b(ι)) stored in the natural number storage 11101. At least part of information on the read pair of natural numbers (a(ι), b(ι)) is sent to the integer computing unit 11103, the input information providing unit 11104, the first power computing unit 11106, and the second power computing unit 11109 (step S11100).

The integer computing unit 11103 uses the sent pair of natural numbers (a(ι), b(ι)) to compute integers a′(ι) and b′(ι) that satisfy the relation a′(ι)a(ι)+b′(ι)b(ι)=1. Since the natural numbers a(ι) and b(ι) are relatively prime to each other, the integers a′(ι) and b′(ι) that satisfy the relation a′(ι)a(ι)+b′(ι)b(ι)=1 definitely exist. Methods for computing such integers are well known. For example, a well-known algorithm such as the extended Euclidean algorithm may be used to compute the integers a′(ι) and b′(ι). Information on the pair of natural numbers (a′(ι), b′(ι)) is sent to the final output unit 11112 (step S11101).

The Controller 11113 Sets t_(ι)=1 (Step S11102).

The input information providing unit 11104 of the computing apparatus 111 generates and outputs first input information τ_(ι,1) and second input information τ_(ι,2) which are elements of a group H_(ι) and each of which corresponds to the input ciphertext λ_(ι). Preferably, the first input information τ_(ι,1) and the second input information τ_(ι,2) are information whose relation with the ciphertext λ_(ι) is scrambled. This enables the computing apparatus 111 to conceal the ciphertext λ_(ι) from the capability providing apparatus 112-ι. Preferably, the first input information τ_(ι,1) of this embodiment further corresponds to the natural number b(ι) selected by the natural number selecting unit 11102 and the second input information τ_(ι,2) further corresponds to the natural number a(ι) selected by the natural number selecting unit 11102. This enables the computing apparatus 111 to evaluate the decryption capability provided by the capability providing apparatus 112-ι with a high degree of accuracy (step S11103).

As illustrated in FIG. 26, the first input information τ_(ι,1) is input in the first output information computing unit 11201-ι of the capability providing apparatus 112-ι (FIG. 20) and the second input information τ_(ι,2) is input in the second output information computing unit 11202-ι (step S11200).

The first output information computing unit 11201 uses the first input information τ_(ι,1) and the decryption key s_(ι) stored in the key storage 11204-ι to correctly compute f_(ι)(τ_(ι,1)) with a probability greater than a certain probability and sets the result of the computation as first output information z_(ι,1) (step S11201). The second output information computing unit 11202-ι uses the second input information τ_(ι,2) and the decryption key s_(ι) stored in the key storage 11204-ι to correctly computes f_(ι)(τ_(ι,2)) with a probability greater than a certain probability and sets the result of the computation as second output information z_(ι,2) (step S11202). Note that the “certain probability” is a probability less than 100%. An example of the “certain probability” is a nonnegligible probability and an example of the “nonnegligible probability” is a probability greater than or equal to 1/ψ(k), where ψ(k) is a polynomial that is a weakly increasing function (non-decreasing function) for a security parameter k. That is, the first output information computing unit 11201-ι and the second output information computing unit 11202-ι can output computation results that have an intentional or unintentional error. In other words, the result of the computation by the first output information computing unit 11201-ι may or may not be f_(ι)(τ_(ι,1)) and the result of the computation by the second output information computing unit 11202-ι may or may not be f_(ι)(τ_(ι,2)). The first output information computing unit 11201-ι outputs the first output information z_(ι,1) and the second output information computing unit 11202-ι outputs the second output information z_(ι,2) (step S11203).

Returning to FIG. 25, the first output information z_(ι,1) is input in the first computing unit 11105 of the computing apparatus 111 (FIG. 19) and the second output information z_(ι,2) is input in the second computing unit 11108. The first output information z_(ι,1) and the second output information z_(ι,2) are equivalent to the decryption capability provided by the capability providing apparatus 112-ι to the computing apparatus 111 (step S11104).

The first computing unit 11105 generates computation result u_(ι)=f_(ι)(λ_(ι))^(b(ι))x_(ι,1) from the first output information z_(ι,1). Here, generating (computing) f_(ι)(λ_(ι))^(b(ι)) _(xι,1) means computing a value of a formula defined as f_(ι)(λ_(ι))^(b(ι))x_(ι,1). Any intermediate computation method may be used, provided that the value of the formula f_(ι)(λ_(ι))^(b(ι)) _(xι,1) can eventually be computed. The same applies to computations of the other formulae that appear herein. The result u_(ι) of the computation is sent to the first power computing unit 11106 (step S11105).

The first power computing unit 11106 computes u_(ι)′=u_(ι) ^(a(ι)). The pair of the result u_(ι) of the computation and u_(ι)′ computed on the basis of the result of the computation, (u_(ι), u_(ι)′), is stored in the first list storage 11107 (step S11106).

The determining unit 11111 determines whether or not there is a pair that satisfies u_(ι)′=v_(ι)′ among the pairs (u_(ι), u_(ι)′) stored in the first list storage 11107 and the pairs (v_(ι), v_(ι)′) stored in the second list storage 11110 (step S11107). If no pair (v_(ι), v_(ι)′) is stored in the second list storage 11110, the process at step S11107 is omitted and the process at step S11108 is performed. If there is a pair that satisfies u_(ι)′=v_(ι)′, the process proceeds to step S11114; if there is not a pair that satisfies u_(ι)′=v_(ι)′, the process proceeds to step S11108.

At step S11108, the second computing unit 11108 generates a computation result v_(ι)=f_(ι)(λ_(ι))^(a(ι))x_(ι,2) from the second output information z_(ι,2). The result v_(ι) of the computation is sent to the second power computing unit 11109 (step S11108).

The second power computing unit 11109 computes v_(ι)′=v_(ι) ^(b(ι)). The pair of the result v_(ι) of the computation and v_(ι)′ computed on the basis of the computation result, (v_(ι), v_(ι)′), is stored in the second list storage 11110 (step S11109).

The determining unit 11111 determines whether or not there is a pair that satisfies u_(ι)′=v_(ι)′ among the pairs (u_(ι), u_(ι)′) stored in the first list storage 11107 and the pairs (v_(ι), v_(ι)′) stored in the second list storage 11110 (step S11110). If there is a pair that satisfies u_(ι)′=v_(ι)′, the process proceeds to step S11114. If there is not a pair that satisfies u_(ι)′=v_(ι)′, the process proceeds to step S11111.

At step S11111, the controller 11113 determines whether or not t_(ι)=T_(ι)(step S11111). Here, T_(ι) is a predetermined natural number. If t_(ι)=T_(ι), the final output unit 11112 outputs information indicating that the computation is impossible, for example the symbol “⊥” (step S11113) and the process ends. If not t_(ι)=T_(ι), the controller 11113 increments t_(ι) by 1, that is, sets t_(ι)=t_(ι)+1 (sets t_(ι)+1 as a new t_(ι)) (step S11112) and the process returns to step S11103.

The information indicating the computation is impossible (the symbol “⊥” in this example) means that the reliability that the capability providing apparatus 112-ι correctly performs computation is lower than a criterion defined by T_(ι). In other words, it means that the capability providing apparatus 112-ι was unable to perform a correct computation in T_(ι) trials.

At step S11114, the final output unit 11112 uses u_(ι) and v_(ι) that correspond to u_(ι)′ and v_(ι)′ that are determined to satisfy u_(ι)′=v_(ι)′ to calculate and output u_(ι) ^(b′(ι))v_(ι) ^(a′(ι)) (step S11114). The u_(ι) ^(b′(ι))v_(ι) ^(a′(ι)) thus computed will be a result f_(ι)(λ_(ι)) of decryption of the ciphertext λ_(ι) with the particular decryption key s_(ι) with a high probability (the reason why u_(ι) ^(b′(ι))v_(ι) ^(a′(ι))=f_(ι)(λ_(ι)) with a high probability will be described later). Therefore, the process described above is repeated multiple times and the value obtained with the highest frequency among the values obtained at step S11114 can be provided as the result of decryption f_(ι)(λ). As will be described later, u_(ι) ^(b′(ι))v_(ι) ^(a′(ι))=f_(ι)(λ_(ι)) can result with an overwhelming probability, depending on settings. In that case, the value obtained at step S11114 can be directly provided as the result of decryption f_(ι)(λ_(ι)).

The decryption results f_(ι)(λ_(ι)) obtained by performing the process described above on each of ι=1, . . . , ω are input in the recovering unit 11100. The recovering unit 11100 uses f_(ι)(λ_(ι))=u_(ι) ^(b′(ι))v_(ι) ^(a′(ι)) for each t=1, . . . , ω to perform a recovering process for obtaining a recovered value that can be recovered only if all of decrypted values that can be obtained by decrypting a ciphertext λ_(ι) for each ι=1, . . . , ω with the decryption key s_(ι) are obtained. For example, if the shares have been generated by <<Example 1 of shares>> described above, the recovering unit 11100 generates a bit combination value f₁(λ₁)| . . . |f_(ω)(λ_(ω)) as the recovered value mes′. For example if the shares have been generated by <<Example 2 of shares>> described above, the recovering unit 11100 generates the exclusive OR of the decryption results f₁(λ₁), . . . , f_(ω)(λ_(ω)) as the recovered value mes′. For example, if the shares have been generated by <<Example 3 of shares>> described above, the recovering unit 11100 generates the recovered value mes' from the decryption results f₁(λ₁), . . . , f_(ω)(λ_(ω)) by using a recovering method corresponding to the secret sharing scheme.

If all of the decryption results f₁(λ₁), . . . , f_(ω)(λ_(ω)) are correct, the recovered value mes′ obtained by the recovering unit 11100 is equal to the message mes. On the other hand, if all of the decryption results f₁(λ₁), . . . , f_(ω)(λ_(ω)) are incorrect, the probability that the recovered value mes′ obtained by the recovering unit 11100 is equal to the message mes is negligibly small.

<<Reason why u_(ι) ^(b′(ι))v_(ι) ^(a′(ι))=f_(ι)(λ_(ι)) with High Probability>>

For simplicity of notation, ι is omitted in the following description.

Let X be a random variable having a value in the group G. For wϵG, an entity that returns wx′ corresponding to a sample x′ according to the random variable X in response to each request is called a sampler having an error X for w.

For wϵG, an entity that returns w^(a)x′ corresponding to a sample x′ according to a random variable X whenever a natural number a is given is called a randomizable sampler having an error X for w. The randomizable sampler functions as the sampler if used with a=1.

The combination of the input information providing unit 11104, the first output information computing unit 11201 and the first computing unit 11105 of this embodiment is a randomizable sampler having an error X₁ for f(λ) (referred to as the “first randomizable sampler”) and the combination of the input information providing unit 11104, the second output information computing unit 11202 and the second computing unit 11108 is a randomizable sampler having an error X₂ for f(λ) (referred to as the “second randomizable sampler”).

The inventor has found that if u′=v′ holds, that is, if u^(a)=v^(b) holds, it is highly probable that the first randomizable sampler has correctly computed u=f(λ)^(b) and the second randomizable sampler has correctly computed v=f(λ)^(a) (x₁ and x₂ are identity elements e_(g) of the group G). For simplicity of explanation, this will be proven in an eleventh embodiment.

When the first randomizable sampler correctly computes u=f(λ)^(b) and the second randomizable sampler correctly computes v=f(λ)^(a) (when x₁ and x₂ are identity elements e_(g) of the group G), then u^(b′)v^(a′)=(f(λ)^(b)x₁)^(b′)(f(λ)^(a)x₂)^(a′)=(f(λ)^(b)e_(g))^(b′)(f(λ)^(a)e_(g))^(a′)=f(λ)^(bb′)e_(g) ^(b′)f(λ)^(aa′)e_(g) ^(a′)=f(λ)^((bb′+aa′))=f(λ).

For (q₁, q₂)ϵI, a function π_(i) is defined by π_(i)(q₁, q₂)=q_(i) for each of i=1, 2. Let L=min (#π₁(S), #π₂(S)), where #⋅ is the order of a set ⋅. If the group G is a cyclic group or a group whose order is difficult to compute, it can be expected that the probability that an output other than “⊥” of the computing apparatus 111 is not f(λ) is at most approximately T² L/#S within a negligible error. If L/#S is a negligible quantity and T is a quantity approximately equal to an polynomial order, the computing apparatus 111 outputs a correct f_(ι)(λ) with an overwhelming probability. An example of S that results in a negligible quantity of L/#S is S={(1, d)|dϵ[2, |G|−1]}.

<Decryption Control Process>

A decryption control process of this embodiment will be described below.

When the decryption control apparatus 113 controls the decryption process performed by the computing apparatus 111, the decryption control apparatus 113 outputs a decryption control instruction that controls the decryption process of the computing apparatus 111 to all of the capability providing apparatuses 112-ι. The capability providing apparatuses 112-ι in which the decryption control instruction is input controls whether to output both of first output information z_(ι,1) and second output information z_(ι,2) according to the input decryption control instruction. The computing apparatus 111 cannot decrypt the ciphertext λ_(ι) unless the first output information z_(ι,1) and second output information z_(ι,2) are provided. Therefore, the decryption capability of the computing apparatus 111 can be controlled by controlling whether or not to output both of the first output information z_(ι,1) and second output information z_(ι,2). Exemplary methods for controlling the decryption process will be described below.

Example 1 of Method for Controlling Decryption Process

In example 1 of the method for controlling the decryption process, decryption control instructions include a decryption restricting instruction com₁-ι for restricting the decryption capability of the computing apparatus 111. When the decryption restricting instruction com₁-ι is input in the controller 11205-ι of the capability providing apparatus 112-ι, the controller 11205-ι prevents output of both of the first output information z_(ι,1) and the second output information z_(ι,2).

To restrict the decryption capability of the computing apparatus 111, the control instruction unit 11302 of the decryption control apparatus 113 (FIG. 21) outputs the decryption restricting instruction com₁-ι for all ι. The decryption restricting instruction com₁-ι is output from the output unit 11303 to the capability providing apparatus 112-ι.

The controller 11205-ι of the capability providing apparatus 112-ι (FIG. 20) determines whether or not the decryption restricting instruction com₁-ι has been input. If the decryption restriction instruction com₁-ι has not been input in the controller 11205-ι, the controller 11205-ι does not perform the decryption control process. On the other hand, if the decryption restriction instruction com₁-ι has been input in the controller 11205-ι, the controller 11205-ι performs control to prevent output of both of the first output information z_(ι,1) and second output information z_(ι,2) (decryption restriction mode).

In the decryption restriction mode, the controller 11205-ι prevents the first output information computing unit 11201-ι from outputting first output information z_(ι,1) and also prevents the second output information computing unit 11202-ι from outputting second output information z_(ι,2). An example of control to prevent output of the first output information z_(ι,1) and/or the second output information z_(ι,2) is control to prevent output of the first output information z_(ι,1) and the second output information z_(ι,2) without preventing generation of the output information z_(ι,1) and/or the second output information z_(ι,2). Another example of control to prevent output of the first output information z_(ι,1) and/or the second output information z_(ι,2) is control to causing the first output information computing unit 11201-ι and the second output information computing unit 11202-ι to output dummy information instead of the first output information z_(ι,1) and/or the second output information z_(ι,2). Note that an example of the dummy information is a random number or other information that is independent of the ciphertexts λ_(ι). Another example of control to prevent output of the first output information z_(ι,1) and/or the second output information z_(ι,2) is control to prevent generation of the first output information z_(ι,1) and the second output information z_(ι,2). If control to prevent generation of the first output information z_(ι,1) and the second output information z_(ι,2) is performed, information required for generating the first output information z_(ι,1) and the second output information z_(ι,2) may optionally be nullified or removed. For example, the decryption key sι stored in the key storage 11204-ι may optionally be nullified or removed.

If output of both of the first output information z_(ι,1) and the second output information z_(ι,2) is prevented, the capability providing apparatus 112-ι outputs neither of the first output information z_(ι,1) and the second output information z_(ι,2) at step S11203. Accordingly, the computing apparatus 111 can obtain neither of the first output information z_(ι,1) and the second output information z_(ι,2) at step S11104 and cannot compute computation results u_(ι) and v_(ι). Therefore a correct decryption result f_(ι)(λ) cannot be obtained. If correct decryption results f_(ι)(λ) cannot be obtained for all ι, the probability that a recovered value mes′ obtained by the recovering unit 11100 will be equal to the message mes is negligibly small. Thus, the decryption capability of the computing apparatus 111 can be restricted.

Example 2 of Method for Controlling Decryption Process

In example 2 of the method for controlling the decryption process, the decryption control instructions include a decryption permitting instruction com₂-ι for removing restriction on the decryption capability of the computing apparatus 111. When the decryption permitting instruction com₂-ι is input in the controller 11205-ι of the capability providing apparatus 112-ι, the controller 11205-ι permits output of at least one of the first output information z_(ι,1) and the second output information z_(ι,2). Example 2 of the method for controlling the decryption process is performed when, for example, output of the first output information z_(ι,1) and the second output information z_(ι,2) is to be permitted again after output of both of first output information z_(ι,1) and second output information z_(ι,2) is prevented by example 1 of the method for controlling the decryption process. In that case, if information required for generating the first output information z_(ι,1) and/or the second output information z_(ι,2) has been nullified or removed, the decryption permitting instruction com₂-ι may include the information and the information may be re-set in the capability providing apparatus 112-ι. Example 2 of the method for controlling the decryption process may also be performed to permit output of the first output information z_(ι,1) and the second output information z_(ι,2) when, for example, output of both of the first output information z_(ι,1) and the second output information z_(ι,2) is prevented in an initial state.

To remove restriction on the decryption capability of the computing apparatus 111, the control instruction unit 11302 of the decryption control apparatus 113 (FIG. 21) outputs the decryption permitting instruction com₂-ι for all ι. The decryption permitting instruction com₂-ι is output from the output unit 11303 to the capability providing apparatus 112-ι.

The controller 11205-ι of the capability providing apparatus 112-ι (FIG. 20) determines whether or not the decryption permitting instruction com₂-ι is input. If the decryption permitting instruction com₂-ι is not input in the controller 11205-ι, the controller 11205-ι does not perform decryption control process. On the other hand, if the decryption permitting instruction com₂-ι is input in the controller 11205-ι, the controller 11205-ι performs control to permit output of both of first output information z_(ι,1) and second output information z_(ι,2) (decryption permission mode).

If output of both of the first output information z_(ι,1) and the second output information z_(ι,2) is permitted, the capability providing apparatus 112-ι outputs both of the first output information z_(ι,1) and the second output information z_(ι,2) at step S11203. Accordingly, the computing apparatus 111 can obtain both of the first output information z_(ι,1) and the second output information z_(ι,2) at step S11104 and can compute u_(ι) or v_(ι) as a computation result. Therefore a correct decryption result can be obtained with a high probability. Thus, the restriction on the decryption capability of the computing apparatus 111 can be removed.

Example 3 of Method for Controlling Decryption Process

In examples 1 and 2 of the method for controlling the decryption process, each decryption control instruction corresponds to any one oft (corresponds to the decryption function f_(ι)) and the controller 11205-ι of the capability providing apparatus 112-ι controls whether to output all of the first output information z_(ι,1) and the second output information z_(ι,2) that correspond to the decryption control instruction (corresponds to the decryption function f_(ι) corresponding to the decryption control instruction). However, a decryption control instruction may correspond to a plurality of ι and the controller 11205-ι of the capability providing apparatus 112-ι may control whether to output the first output information z_(ι,1) and the second output information z_(ι,2) corresponding to the decryption control instruction.

Eighth Embodiment

A proxy computing system of an eighth embodiment is an example that embodies the first randomizable sampler and the second randomizable sampler described above. The following description will focus on differences from the seventh embodiment and repeated description of commonalities with the seventh embodiment, including the decryption control process, will be omitted. In the following description, elements labeled with the same reference numerals have the same functions and the steps labeled with the same reference numerals represent the same processes.

<Configuration>

As illustrated in FIG. 18, the proxy computing system 102 of the eighth embodiment includes a computing apparatus 121 in place of the computing apparatus 111 and capability providing apparatuses 122-1, . . . , 122-Γ in place of the capability providing apparatuses 112-1, . . . , 112-Γ.

As illustrated in FIG. 19, the computing apparatus 121 of the eighth embodiment includes, for example, a natural number storage 11101, a natural number selecting unit 11102, an integer computing unit 11103, an input information providing unit 12104, a first computing unit 12105, a first power computing unit 11106, a first list storage 11107, a second computing unit 12108, a second power computing unit 11109, a second list storage 11110, a determining unit 11111, a final output unit 11112 and a controller 11113. As illustrated in FIG. 22, the input information providing unit 12104 of this embodiment includes, for example, a first random number generator 12104 a, a first input information computing unit 12104 b, a second random number generator 12104 c, and a second input information computing unit 12104 d.

As illustrated in FIG. 20, the capability providing apparatus 122-ι of the eighth embodiment (ι=1, . . . , ω, where ω is an integer greater than or equal to 2 and less than or equal to Γ) includes, for example, a first output information computing unit 12201-ι, a second output information computing unit 12202-ι, a key storage 11204-ι, and a controller 11205-ι. If there are capability providing apparatuses 122-(ι+1), . . . , 122-Γ, the capability providing apparatuses 122-(ι+1), . . . , 122-Γ have the same configuration as the capability providing apparatus 122-ι.

<Decryption Process>

A decryption process of this embodiment will be described below. In the eighth embodiment, a decryption function f_(ι) is a homomorphic function, a group H is a cyclic group, and a generator of the group H is μ_(ι,h), the order of the group H is K_(ι,H), and v_(ι)=f_(ι)(μ_(ι,h)). An example of cryptography in which the decryption function f_(ι) is a homomorphic function is RSA cryptography. The rest of the assumptions are the same as those in the seventh embodiment, except that the computing apparatus 111 is replaced with the computing apparatus 121 and the capability providing apparatuses 112-1, . . . , 112-Γ are replaced with the capability providing apparatuses 122-1, . . . , 122-Γ.

As illustrated in FIGS. 25 and 26, the process of the eighth embodiment is the same as the process of the seventh embodiment except that steps S11103 through S11105, S11108, and S11200 through S11203 of the seventh embodiment are replaced with steps S12103 through S12105, S12108, and S12200 through S12203, respectively. In the following, only processes at steps S12103 through S12105, S12108, and S12200 through S12203 will be described.

<<Process at Step S12103>>

The input information providing unit 12104 of the computing apparatus 121 (FIG. 19) generates and outputs first input information τ_(ι,1) and second input information τ_(ι,2) which corresponds to an input ciphertext λ_(ι) (step S12103 of FIG. 25). A process at step S12103 of this embodiment will be described with reference to FIG. 27.

The first random number generator 12104 a (FIG. 22) generates a uniform random number r(ι, 1) that is a natural number greater than or equal to 0 and less than K_(ι,H). The generated random number r(ι, 1) is sent to the first input information computing unit 12104 b and the first computing unit 12105 (step S12103 a). The first input information computing unit 12104 b uses the input random number r(ι, 1), the ciphertext λ_(ι) and a natural number b(ι) to compute first input information τ_(ι,1)=μ_(ι,h) ^(r(ι,1))λ_(ι) ^(b(ι)) (step S12103 b).

The second random number generator 12104 c generates a uniform random number r(ι, 2) that is a natural number greater than or equal to 0 and less than K_(ι,H). The generated random number r(ι, 2) is sent to the second input information computing unit 12104 d and the second computing unit 12108 (step S12103 c). The second input information computing unit 12104 d uses the input random number r(ι, 2), the ciphertext λ_(ι), and a natural number a(ι) to compute second input information τ_(ι,2)=μ_(ι,h) ^(r(ι,2))λ_(ι) ^(a(ι)) (step S12103 d).

The first input information computing unit 12104 b and the second input information computing unit 12104 d output the first input information τ_(ι,1) and the second input information τ_(ι,2) thus generated (step S12103 e). Note that the first input information τ_(ι,1) and the second input information τ_(ι,2) in this embodiment are information whose relation with the ciphertext λ_(ι) is scrambled by using random numbers r(ι, 1) and r(ι, 2), respectively. This enables the computing apparatus 121 to conceal the ciphertext λ_(ι) from the capability providing apparatus 122-ι. The first input information τ_(ι,1) in this embodiment further corresponds to the natural number b(ι) selected by the natural number selecting unit 11102 and the second input information ι_(ι,2) further corresponds to the natural number a(ι) selected by the natural number selecting unit 11102. This enables the computing apparatus 121 to evaluate the decryption capability provided by the capability providing apparatus 122-ι with a high degree of accuracy.

<<Processes at Steps S12200 Through S12203>>

As illustrated in FIG. 26, first, the first input information τ_(ι,1)=μ_(ι,h) ^(r(ι,1))λ_(ι) ^(b(ι)) is input in the first output information computing unit 12201-ι of the capability providing apparatus 122-ι (FIG. 20) and the second input information τ_(ι,2)=μ_(ι,h) ^(r(ι,1))λ_(ι) ^(a(ι)) is input in the second output information computing unit 12202-ι (step S12200).

The first output information computing unit 12201-ι uses the first input information τ_(ι,1)=μ_(ι,h) ^(r(ι,1))λ_(ι) ^(b(ι)) and a decryption key s_(ι) stored in the key storage 11204-ι to correctly compute f_(ι)(μ_(ι,h) ^(r(ι,1))λ_(ι) ^(b(ι))) with a probability greater than a certain probability and sets the result of the computation as first output information z_(ι,1). The result of the computation may or may not be correct. That is, the result of the computation by the first output information computing unit 12201-ι may or may not be f_(ι)(μ_(ι,h) ^(r(ι,1))λ_(ι) ^(b(ι))) (step S12201).

The second output information computing unit 12202-ι uses the second input information τ_(ι,2)=μ_(ι,h) ^(r(ι,1))λ_(ι) ^(a(ι)) and the decryption key s_(ι) stored in the key storage 11204-ι to correctly compute f_(ι)(μ_(ι,h) ^(r(ι,1))λ_(ι) ^(a(ι))) with a probability greater than a certain probability and provides the result of the computation as second output information z_(ι,2). The result of the computation may or may not be correct. That is, the result of the computation by the second output information computing unit 12202-ι may or may not be f_(ι)(μ_(ι,h) ^(r(ι,1))λ_(ι) ^(a(ι))) (step S12202).

The first output information computing unit 12201-ι outputs the first output information z_(ι,1) and the second output information computing unit 12202-ι outputs the second output information z_(ι,2) (step S12203).

<<Processes at Steps S12104 and S12105>>

Returning to FIG. 25, the first output information z_(ι,1) is input in the first computing unit 12105 of the computing apparatus 121 (FIG. 19) and the second input information z_(ι,2) is input in the second computing unit 12108. The first output information z_(ι,1) and the second output information z_(ι,2) are equivalent to the decryption capability provided by the capability providing apparatus 122-ι to the computing apparatus 121 (step S12104).

The first computing unit 12105 uses the input random number r(ι, 1) and the first output information z₁, to compute z_(ι,1)ν₁ ^(−r(ι,1)) and sets the result of the computation as u_(ι). The result u_(ι) of the computation is sent to the first power computing unit 11106. Here, u_(ι)=z_(ι,1)v_(ι) ^(−r(ι,1))=f_(ι)(λ_(ι))^(b(ι))x_(ι,1). That is, Z_(ι,1)ν_(ι) ^(−r(ι,1)) is an output of a randomizable sampler having an error X_(ι,1) for f_(ι)(λ_(ι)). The reason will be described later (step S12105).

<<Process at Step S12108>>

The second computing unit 12108 uses the input random number r(ι, 2) and the second output information z_(ι,2) to compute z_(ι,2)v_(ι) ^(−r(ι,2)) and sets the result of the computation as v_(ι). The result v_(ι) of the computation is sent to the second power computing unit 11109. Here, v_(ι)=z_(ι,2)ν_(ι) ^(−r(ι,2))=f_(ι)(λ_(ι))^(a(ι))x_(ι2). That is, z_(ι,2)ν₁ ^(−r(ι,2)) is an output of a randomizable sampler having an error X_(ι,2) for f_(ι)(λ_(ι)). The reason will be described later (step S12108).

<<Reason why z_(ι,1)ν_(ι) ^(−r(ι,1)) and z_(ι,2)ν_(ι) ^(−r(ι,2)) are Outputs of Randomizable Samplers Having Errors X_(ι,1) and X_(ι,2), Respectively, for f_(ι)(λ_(ι))>>

Let c be a natural number, R be a random number, and B(μ_(h) ^(R)λ^(c)) be the result of computation performed by the capability providing apparatus 122 using μ_(h) ^(R)λ^(c). That is, the results of computations that the first output information computing unit 12201-ι and the second output information computing unit 12202-ι return to the computing apparatus 121 are z=B(μ_(h) ^(R)λ^(c)). A random variable X that has a value in the group G is defined as X=B(μ_(h) ^(R′))f(μ_(h) ^(R′))⁻¹.

Then, zν^(−R)=B(μ_(h) ^(R)λ^(c))f(μ_(h))^(−R)=Xf(μ_(h) ^(R)λ^(c))f(μ_(h))^(−R)=Xf(μ_(h))^(R)f(λ)^(c)f(μ_(h))^(−R)=f(λ)^(c)X. That is, zν^(−R) is an output of a randomizable sampler having an error X for f(λ).

The expansion of formula given above uses the properties such that X=B(μ_(h) ^(R′))f(μ_(h) ^(R′))⁻¹=B(μ_(h) ^(R)λ^(c))f(μ_(h) ^(R)λ^(c))⁻¹ and that B(μ_(h) ^(R)λ^(c))=Xf(μ_(h) ^(R)λ^(c)). The properties are based on the fact that the function f_(ι) is a homomorphic function and R is a random number.

Therefore, considering that a(ι) and b(ι) are natural numbers and r(ι, 1) and r(ι, 2) are random numbers, z_(ι,1)ν_(ι) ^(−r(ι,1)) and z_(ι,2)ν_(ι) ^(−r(ι,2)) are, likewise, outputs of randomizable samplers having errors X_(ι,1) and X_(ι,2), respectively, for f_(ι)(λ_(ι)).

Ninth Embodiment

A ninth embodiment is a variation of the eighth embodiment and computes a value of u_(ι) or v_(ι) by using samplers described above when a(ι)=1 or b(ι)=1. The amounts of computation performed by samplers in general are smaller than randomizable samplers. Using samplers instead of randomizable samplers for computations when a(ι)=1 or b(ι)=1 can reduce the amount of computation by the proxy computing system. The following description will focus on differences from the seventh and eighth embodiments and repeated description of commonalities with the seventh and eighth embodiments, including the decryption control process, will be omitted.

<Configuration>

As illustrated in FIG. 18, a proxy computing system 103 of the ninth embodiment includes a computing apparatus 131 in place of the computing apparatus 121 and capability providing apparatuses 132-1, . . . , 132-Γ in place of the capability providing apparatuses 122-1, . . . , 122-Γ.

As illustrated in FIG. 19, the computing apparatus 131 of the ninth embodiment includes, for example, a natural number storage 11101, a natural number selecting unit 11102, an integer computing unit 11103, an input information providing unit 12104, a first computing unit 12105, a first power computing unit 11106, a first list storage 11107, a second computing unit 12108, a second power computing unit 11109, a second list storage 11110, a determining unit 11111, a final output unit 11112, a controller 11113, and a third computing unit 13109.

As illustrated in FIG. 20, the capability providing apparatus 132-ι of the ninth embodiment includes, for example, a first output information computing unit 12201-ι, a second output information computing unit 12202-ι, a key storage 11204-ι, a controller 11205-ι, and a third output information computing unit 13203-ι.

<Decryption Process>

A decryption process of this embodiment will be described below. Differences from the eighth embodiment will be described.

As illustrated in FIGS. 25 and 26, the process of the ninth embodiment is the same as the process of the eighth embodiment except that steps S12103 through S12105, S12108, and S12200 through S12203 of the eighth embodiment are replaced with steps S13103 through S13105, S13108, S12200 through S12203, and S13205 through 13209, respectively. The following description will focus on processes at steps S13103 through S13105, S13108, S12200 through S12203, and S13205 through S13209.

<<Process at Step S13103>>

The input information providing unit 13104 of the computing apparatus 131 (FIG. 19) generates and outputs first input information τ_(ι,1) and second input information τ_(ι,2) each of which corresponds to a input ciphertext λ_(ι)(step S13103 of FIG. 25).

A process at S13103 of this embodiment will be described below with reference to FIG. 27.

The controller 11113 (FIG. 19) controls the input information providing unit 13104 according to natural numbers (a(ι), b(ι)) selected by the natural number selecting unit 11102.

Determination is made by the controller 11113 as to whether b is equal to 1 (step S13103 a). If it is determined that b≠1, the processes at steps S12103 a and 12103 b described above are performed and the process proceeds to step S13103 g.

On the other hand, if it is determined at step S13103 a that b(ι)=1, the third random number generator 13104 e generates a random number r(ι3) that is a natural number greater than or equal to 0 and less than K_(ι,II). The generated random number r(ι, 3) is sent to the third input information computing unit 13104 f and the third computing unit 13109 (step S13103 b). The third input information computing unit 13104 f uses the input random number r(ι, 3) and the ciphertext λ_(ι) to compute λ_(ι) ^(r(ι,3)) and sets it as first input information τ_(ι,1)(step S13103 c). Then the process proceeds to step S13103 g.

At step S13103 g, determination is made by the controller 11113 as to whether a(ι) is equal to 1 (step S13103 g). If it is determined that a(ι)≠1, the processes at steps S12103 c and S12103 d described above are performed.

On the other hand, if it is determined at step S13103 g that a(ι)=1, the third random number generator 3104 e generates a random number r(ι, 3) that is a natural number greater than or equal to 0 and less than K_(ι,H). The generated random number r(ι, 3) is sent to the third input information computing unit 13104 f (step S13103 h). The third input information computing unit 13104 f uses the input random number r(ι, 3) and the ciphertext λ_(ι) to compute λ_(ι) ^(r(ι,3)) and sets it as second input information τ_(ι,2) (step S13103 i).

The first input information computing unit 12104 b, the second input information computing unit 12104 d, and the third input information computing unit 13104 f output the first input information τ_(ι,1) and the second input information τ_(ι,2) thus generated along with information on corresponding natural numbers (a(ι), b(ι)) (step S13103 e). Note that the first input information τ_(ι,1) and the second input information τ_(ι,2) in this embodiment are information whose relation with the ciphertext λ_(ι) is scrambled by using the random numbers r(ι, 1), r(ι, 2), and r(ι, 3), respectively. This enables the computing apparatus 131 to conceal the ciphertext λ_(ι) from the capability providing apparatus 132-ι.

<<Processes at S12200 Through S12203 and S13205 Through S13209>>

Processes at S12200 through S12203 and S13205 through S13209 of this embodiment will be described below with reference to FIG. 26.

The controller 11205-ι (FIG. 20) controls the first output information computing unit 12201-ι, the second output information computing unit 12202-ι, and the third output information computing unit 13203-ι according to input natural numbers (a(ι), b(ι)).

Under the control of the controller 11205-ι, the first input information τ_(ι,1)=μ_(ι,h) ^(r(ι,1))λ_(ι) ^(b(ι)) when b(ι)≠1 is input in the first output information computing unit 12201-ι of the capability providing apparatus 132-ι (FIG. 20) and the second input information τ_(ι,2)=μ_(ι,h) ^(r(ι,2))λ_(ι) ^(a(ι)) when a(ι)≠1 is input in the second output information computing unit 12202-ι. The first input information τ_(ι,1)=λ_(ι) ^(r(ι,3)) when b(ι)=1 and the second input information τ_(ι,2)=λ_(ι) ^(r(ι,3)) when a(ι)=1 are input in the third output information computing unit 13203-ι (step S13200).

Determination is made by the controller 11113 as to whether b(ι) is equal to 1 (step S13205). If it is determined that b(ι)≠1, the process at step S12201 described above is performed. Then, determination is made by the controller 11113 as to whether a(ι) is equal to 1 (step S13208). If it is determined that a(ι)≠1, the process at step S12202 described above is performed and then the process proceeds to step S13203.

On the other hand, if it is determined at step S13208 that a(ι)=1, the third output information computing unit 13203-ι uses the second input information τ_(ι,2)=λ_(ι) ^(r(ι,3)) to correctly compute f_(ι)(λ_(ι) ^(r(ι,3))) with a probability greater than a certain probability and sets the obtained result of the computation as third output information z_(ι,3). The result of the computation may or may not be correct. That is, the result of the computation by the third output information computing unit 13203-ι may or may not be f_(ι)(λ_(ι) ^(r(ι,3))) (step S13209). Then the process proceeds to step S13203.

If it is determined at step S13205 that b(ι)=1, the third output information computing unit 13203-ι uses the second input information τ_(ι,1)=λ_(ι) ^(r(ι,3)) to correctly compute f_(ι)(λ_(ι) ^(r(ι,3))) with a probability greater than a certain probability and sets the obtained result of the computation as third output information z_(ι,3). The result of the computation may or may not be correct. That is, the result of the computation by the third output information computing unit 13203-ι may or may not be f_(ι)(λ_(ι) ^(r(ι,3))) (step S13206).

Then, determination is made by the controller 11113 as to whether a(ι) is equal to 1 (step S13207). If it is determined that a(ι)=1, the process proceeds to step S13203; if it is determined a(ι)≠1, the process proceeds to step S12202.

At step S13203, the first output information computing unit 12201-ι, which has generated the first output information z_(ι,1), outputs the first output information z_(ι,1), the second output information computing unit 12202-ι, which has generated the second output information z_(ι,2), outputs the second output information z_(ι,2), and the third output information computing unit 13203-ι, which has generated the third output information z_(ι,3), outputs the third output information z_(ι,3) (step S13203).

<<Processes at Steps S13104 and S13105>>

Returning to FIG. 25, under the control of the controller 11113, the first output information z_(ι,1) is input in the first computing unit 12105 of the computing apparatus 131 (FIG. 19), the second output information z_(ι,2) is input in the second computing unit 12108, and the third output information z_(ι,3) is input in the third computing unit 13109 (step S13104).

If b(ι)≠1, the first computing unit 12105 performs the process at step S12105 described above to generate u_(ι); if b(ι)=1, the third computing unit 13109 computes z_(ι,3) ^(1/r(ι,3)) and sets the result of the computation as u_(ι). The result u_(ι) of the computation is sent to the first power computing unit 11106. Here, if b(ι)=1, then u_(ι)=z_(ι,3) ^(1/r(ι,3))=f_(ι)(λ_(ι))x_(ι,3). That is, z_(ι,1) ^(1/r(ι,3)) serves as a sampler having an error X_(ι,3) for f_(ι)(λ_(ι)). The reason will be described later (step S13105).

<<Process at Step S13108>>

If a(ι)≠1, the second computing unit 12108 performs the process at step S12108 to generate v_(ι); if a(ι)=1, the third computing unit 13109 computes z_(ι,3) ^(1/r(ι,3)) and sets the result of the computation as v_(ι). The result v_(ι) of the computation is sent to the second power computing unit 11109. Here, if a(ι)=1, then v_(ι)=^(1/r(ι,3))=f_(ι)(λ_(ι))X_(ι,3). That is, z_(ι,3) ^(1/r(ι,3)) serves as a sampler having an error X_(ι,3) for f_(ι)(λ_(ι)). The reason will be described later (step S13108).

Note that if z_(ι,3) ^(1/r(ι,3)), that is, the radical root of z_(ι,1), is hard to compute, u_(ι) and/or v_(ι) may be calculated as follows. The third computing unit 13109 may store each pair of the random number r(ι, 3) and z_(ι,1) computed on the basis of the random number r(ι, 3) in a storage, not depicted, in sequence as (α₁, β₁), (α₂, β₂), . . . , (α_(m(ι)), β_(m(ι))), . . . . Here, m(ι) is a natural number greater than or equal to 1. The third computing unit 13109 may compute γ₁, γ₂, . . . , γ_(m(ι)) that satisfies γ₁α₁+γ₂α₂+ . . . +γ_(m(ι))α_(m(ι))=1 if the least common multiple of α₁, α₂, α_(m(ι)) is 1, where γ₁, γ₂, . . . , γ_(m(ι)) are integers. The third computing unit 13109 then may use the resulting γ₁, γ₂, . . . , γ_(m(ι)) to compute Π_(i=1) ^(m(ι))β_(i) ^(γi)=β₁ ^(γ1)β₂ ^(γ2) . . . β_(m(ι)) ^(γm(ι)) and may set the results of the computation as u_(ι) and/or v_(ι). Note that when a notation α^(βγ) is used herein in this way, βγ represents β_(γ), namely β with subscript γ, where α is a first letter, β is a second letter, and γ is a number.

<<Reason why z_(ι,3) ^(1/r(ι,3)) Serves as a Sampler Having an Error X_(ι,3) for f_(ι)(λ_(ι))>>

Let R and R′ be random numbers and B(λ^(R)) be the result of computation performed by the capability providing apparatus 132-ι by using λ^(R). That is, let z=B(x^(R)) be the results of computations returned by the first output information computing unit 12201-ι, the second output information computing unit 12202-ι, and the third output information computing unit 13203-ι to the computing apparatus 131. Furthermore, a random variable X having a value in the group G is defined as X=B(λ^(R))^(1/R)f(λ)⁻¹.

Then z^(1/R)=B(λ^(R))^(1/R)=Xf(λ)=f(λ)X. That is, z^(1/R) serves as a sampler having an error X for f(λ).

The expansion of formula given above uses the properties such that X=B(λ^(R))^(1/R)f(λ,R)⁻¹ and that B(λ^(R))^(1/R)=Xf(λR). The properties are based on the fact that R and R′ are random numbers.

Therefore, considering that r(ι, 3) is a random number, z_(ι,3) ^(1/r(ι,3)) serves as a sampler having an error X_(ι,3) for f_(ι)(λ_(ι)), likewise.

Tenth Embodiment

A proxy computing system of a tenth embodiment is another example that embodies the first and second randomizable samplers described above. Specifically, the proxy computing system embodies an example of the first and second randomizable samplers in the case where H_(ι)=G_(ι)×G_(ι) and the decryption function f_(ι) is a decryption function of ElGamal encryption, that is, f_(ι)(c_(ι,1), c_(ι,2))=c_(ι,1)·c_(ι,2) ^(−sι) for a decryption key s_(ι) and a ciphertext λ_(ι)=(c_(ι,1), c_(ι,2)). The following description will focus on differences from the seventh embodiment and repeated description of commonalities with the seventh embodiment, including the decryption control process, will be omitted.

As illustrated in FIG. 18, the proxy computing system 104 of the tenth embodiment includes a computing apparatus 141 in place of the computing apparatus 111 and capability providing apparatuses 142-1, . . . , 142-Γ in place of the capability providing apparatuses 112-1, . . . , 112-Γ.

As illustrated in FIG. 19, the computing apparatus 141 of the tenth embodiment includes, for example, a natural number storage 11101, a natural number selecting unit 11102, an integer computing unit 11103, an input information providing unit 14104, a first computing unit 14105, a first power computing unit 11106, a first list storage 11107, a second computing unit 14108, a second power computing unit 11109, a second list storage 11110, a determining unit 11111, a final output unit 11112, and a controller 11113. As illustrated in FIG. 23, the input information providing unit 14104 of this embodiment includes, for example, a fourth random number generator 14104 a, a fifth random number generator 14104 b, a first input information computing unit 14104 c, a sixth random number generator 14104 d, a seventh random number generator 14104 e, and a second input information computing unit 14104 f. The first input information computing unit 14104 c includes, for example, a fourth input information computing unit 14104 ca and a fifth input information computing unit 14104 cb. The second input information computing unit 14104 f includes, for example, a sixth input information computing unit 14104 fa and a seventh input information computing unit 14104 fb.

As illustrated in FIG. 20, the capability providing apparatus 142-ι of the tenth embodiment includes, for example, a first output information computing unit 14201-ι, a second output information computing unit 14202-ι, a key storage 11204-ι, and a controller 11205-ι. If there are capability providing apparatuses 142-(ι+1), . . . , 42-Γ, the capability providing apparatuses 142-(ι+1), . . . , 142-Γ have the same configuration as the capability providing apparatus 142-ι.

<Decryption Process>

A decryption process of this embodiment will be described below. In the tenth embodiment, it is assumed that a group H_(ι) is the direct product group G_(ι)×G_(ι) of a group G_(ι), the group G_(ι) is a cyclic group, a ciphertext λ_(ι)=(c_(ι,1), c_(ι,2))ϵH_(ι), f_(ι)(c_(ι,1), c_(ι,2)) is a homomorphic function, a generator of the group G_(ι) is μ_(ι,g), the order of the group G_(ι) is K_(ι,G), a pair of a ciphertext (V_(ι), W_(ι))ϵH_(ι) and a decrypted text f_(ι)(V_(ι), W_(ι))=Y_(ι)ϵG_(ι) decrypted from the ciphertext for the same decryption key s_(ι) is preset in the computing apparatus 141 and the capability providing apparatus 142-ι, and the computing apparatus 141 and the capability providing apparatus 142-ι can use the pair.

As illustrated in FIGS. 25 and 26, the process of the tenth embodiment is the same as the process of the seventh embodiment except that steps S11103 through S11105, S11108, and S11200 through S11203 of the seventh embodiment are replaced with steps S14103 through S14105, S14108, and S14200 through S14203, respectively. In the following, only processes at steps S14103 through S14105, S14108, and S14200 through S14203 will be described.

<<Process at Step S14103>>

The input information providing unit 14104 of the computing apparatus 141 (FIG. 19) generates and outputs first input information τ_(ι,1) corresponding to an input ciphertext λ_(ι)=(c_(ι,1), c_(ι,2)) and second input information τ_(ι,2) corresponding to the ciphertext λ_(ι)=(c_(ι,1), c_(ι,2)) (step S14103 of FIG. 25). A process at step S14103 of this embodiment will be described below with reference to FIG. 28.

The fourth random number generator 14104 a (FIG. 23) generates a uniform random number r(ι,4) that is a natural number greater than or equal to 0 and less than K_(ι,G). The generated random number r(ι, 4) is sent to the fourth input information computing unit 14104 ca, the fifth input information computing unit 14104 cb, and the first computing unit 14105 (step S14103 a). The fifth random number generator 14104 b generates a uniform random number r(ι, 5) that is a natural number greater than or equal to 0 and less than K_(ι,G). The generated random number r(ι, 5) is sent to the fifth input information computing unit 14104 cb and the first computing unit 14105 (step S14103 b).

The fourth input information computing unit 14104 ca uses a natural number b(ι) selected by the natural number selecting unit 11102, c_(ι,2) included in the ciphertext λ_(ι), and the random number r(ι, 4) to compute fourth input information c_(ι,2) ^(b(ι))W_(ι) ^(r(ι,4)) (step S14103 c). The fifth input information computing unit 14104 cb uses the natural number b(ι) selected by the natural number selecting unit 11102, c_(ι,1) included in the ciphertext λ_(ι), and random numbers r(ι, 4) and r(ι, 5) to compute fifth input information c_(ι,1) ^(b(ι))V_(ι) ^(r(ι,4))μ_(ι,g) ^(r(ι,5)) (step S14103 d).

The sixth random number generator 14104 d generates a uniform random number r(ι, 6) that is a natural number greater than or equal to 0 and less than K_(ι,G). The generated random number r(ι, 6) is sent to the sixth input information computing unit 14104 fa, the seventh input information computing unit 14104 fb, and the second computing unit 14108 (step S14103 e). The seventh random number generator 14104 e generates a uniform random number r(ι 7) that is a natural number greater than or equal to 0 and less than K_(ι,G). The generated random number r(ι, 7) is sent to the seventh input information computing unit 14104 fb and the second computing unit 14108 (step S14103 f).

The sixth input information computing unit 14104 fa uses a natural number a(ι) selected by the natural number selecting unit 11102, c_(ι,2) included in the ciphertext λ_(ι), and the random number r(ι, 6) to compute sixth input information c_(ι,2) ^(a(ι))W_(ι) ^(r(ι,6)) (step S14103 g). The seventh input information computing unit 14104 fb uses a natural number a(ι) selected by the natural number selecting unit 11102, c_(ι,1) included in the ciphertext λ₁, and the random numbers r(ι, 6) and r(ι, 7) to compute seventh input information c_(ι,1) ^(a(ι))V_(ι) ^((ι,6))μ_(ι,g) ^(r(ι,7)) (step S14103 h).

The first input information computing unit 14104 c outputs the fourth input information c_(ι,2) ^(b(ι))W_(ι) ^(r(ι,4)) and the fifth input information c_(ι,1) ^(b(ι))V_(ι) ^(r(ι,4))μ_(ι,g) ^(r(ι,5)) generated as described above as first input information τ_(ι,1)=(c_(ι,2) ^(b(ι))W_(ι) ^(r(ι,4)), c_(ι,1) ^(b(ι))V_(ι) ^(r(ι,4))μ_(ι,g) ^(r(ι,5))). The second input information computing unit 14104 f outputs the sixth input information c_(ι,2) ^(a(ι))W_(ι) ^(r(ι,6)) and the seventh input information c_(ι,1) ^(a(ι))V_(ι) ^(r(ι,6))μ_(ι,g) ^(r(ι,7)) generated as described above as second input information τ_(ι,2)=(c_(ι,2) ^(a(ι))W_(ι) ^(r(ι,6)), c_(ι,1) ^(a(ι))V_(ι) ^(r(ι,6))μ_(g) ^(r(ι,7))) (step S14103 i).

<<Processes at Steps S14200 Through S14203>>

As illustrated in FIG. 26, first, the first input information τ_(ι,1)=(C_(ι,2) ^(b(t))W_(ι) ^(r(ι,4)), c_(ι,1) ^(b(t))V_(ι) ^(r(ι,4))μ_(ι,g) ^(r(ι,5))) is input in the first output information computing unit 14201-ι of the capability providing apparatus 142-ι (FIG. 20) and the second input information τ_(ι,2)=(c_(ι,2) ^(a(ι))W_(ι) ^(r(ι,6)), c_(ι,1) ^(a(ι))V_(ι) ^(r(ι,6))μ_(ι,g) ^(r(ι,7))) is input in the second output information computing unit 14202-ι (step S14200).

The first output information computing unit 14201-ι uses the first input information τ_(ι,1)=(c_(ι,2) ^(b(ι))W_(ι) ^(r(ι,4)), c_(ι,1) ^(b(ι))V_(ι) ^(r(ι,4))μ_(ι,g) ^(r(ι,5))) and the decryption key s_(ι) stored in the key storage 11204-ι to correctly compute f_(ι)(c_(ι,1) ^(b(ι))V_(ι) ^(r(ι,4))μ_(ι,g) ^(r(ι,5)), c_(ι,2) ^(b(ι))W_(ι) ^(r(ι,4))) with a probability greater than a certain probability and sets the result of the computation as first output information z_(ι,1). The result of the computation may or may not be correct. That is, the result of the computation by the first output information computing unit 14201-ι may or may not be f_(ι)(c_(ι,1) ^(b(ι))V_(ι) ^(r(ι,4))μ_(ι,g) ^(r(ι,5)), c_(ι,2) ^(b(ι))W_(ι) ^(r(ι,4))) (step S14201).

The second output information computing unit 14202-ι can correctly compute f_(ι)(c_(ι,1) ^(a(ι))V_(ι) ^(r(ι,6))μ_(ι,g) ^(r(ι,7)), c_(ι,2) ^(a(ι))W_(ι) ^(r(ι,6))) with a probability greater than a certain probability by using the second input information τ_(ι,2)=(c_(ι,2) ^(a(ι))W_(ι) ^(r(ι,6)), c_(ι,1) ^(a(ι))V_(ι) ^(r(ι,6))μ_(ι,g) ^(r(ι,7))) and the decryption key s_(ι) stored in the key storage 11204-ι and provides the result of the computation as second input information z_(ι,2). The result of the computation may or may not be correct. That is, the result of the computation by the second output information computing unit 14202-ι may or may not be f_(ι)(c_(ι,1) ^(a(ι))V_(ι) ^(r(ι,6))μ_(ι,g) ^(r(ι,7)), c_(ι,2) ^(a(ι))W_(ι) ^(r(ι,6))) (step S14202).

The first output information computing unit 14201-ι outputs the first output information z_(ι,1) and the second output information computing unit 14202-ι outputs the second output information z_(ι,2) (step S14203).

<<Processes at Steps S14104 and S14105>>

Returning to FIG. 25, the first output information z_(ι,1) is input in the first computing unit 14105 of the computing apparatus 141 (FIG. 19) and the second output information z_(ι,2) is input in the second computing unit 14108 (step S14104).

The first computing unit 14105 uses the input first output information z_(ι,1) and the random numbers r(ι, 4) and r(ι, 5) to compute z_(ι,1)Y_(ι) ^(−r(ι,4))μ_(ι,g) ^(−r(ι,5)) and sets the result of the computation as u_(ι)(step S14105). The result u_(ι) of the computation is sent to the first power computing unit 11106. Here, u_(ι)=z_(ι,1)Y_(ι) ^(−r(ι,4))μ_(ι,g) ^(−r(ι,5))=f_(ι)(C_(ι,1), C_(ι,2))^(b(ι))x_(ι,1). That is, z_(ι,1)Y_(ι) ^(−r(ι,4))μ_(ι,g) ^(−r(ι,5)) is an output of a randomizable sampler having an error X_(ι,1) for f_(ι)(c_(ι,1), c_(ι,2)). The reason will be described later.

<<Process at Step S14108>>

The second computing unit 14108 uses the input second output information z_(ι,2) and the random numbers r(ι, 6) and r(ι, 7) to compute Z_(ι,2)Y_(ι) ^(−r(ι,6))μ_(ι,g) ^(−r(ι,7)) and sets the result of the computation as v_(ι). The result v_(ι) of the computation is sent to the second power computing unit 11109. Here, v_(ι)=z_(ι,2)Y_(ι) ^(−r(ι,6))μ_(ι,g) ^(−r(ι,7))=f_(ι)(_(ι,1), c_(ι,2))^(a(ι))x_(ι,2). That is, Z_(ι,2)Y_(ι) ^(−r(ι,6))μ_(ι,g) ^(−r(ι,7)) is an output of a randomizable sampler having an error X_(ι,2) for f_(ι)(c_(ι,1), c_(ι,2)). The reason will be described later.

<<Reason why z_(ι,1)Y^(−r(ι,4))μ_(ι,g) ^(−r(ι,5)) and z_(ι,2)Y_(ι) ^(−r(ι,6))μ_(ι,g) ^(−r(ι,7)) are Outputs of Randomizable Samplers Having Errors X_(ι,1) and X_(ι,2), Respectively, for (c_(ι,1), c_(ι,2))>>

Let c be a natural number, R₁, R₂, R₁′ and R₂′ be random numbers, and B(c₁ ^(c)V^(R1)μ_(g) ^(R2), c₂ ^(c)W^(R1)) be the result of computation performed by the capability providing apparatus 142-ι by using c₁ ^(c)V^(R1)μ_(g) ^(R2) and c₂ ^(c)W^(R1). That is, the first output information computing unit 14201-ι and the second output information computing unit 14202-ι return z=B(c₁ ^(c)V^(R1)μ_(g) ^(R2), c₂ ^(c)W^(R1)) as the results of computations to the computing apparatus 141. A random variable X having a value in a group G is defined as X=B(V^(R1′)μ_(g) ^(R2′),W^(R1′))f(V^(R1′)μ_(g) ^(R2′), W^(R1′))⁻¹.

Then, zY^(−R1)μ_(g) ^(−R2)=B(c₁ ^(c)V^(R1)μ_(g) ^(R2), c₂ ^(c)W^(R1))Y^(−R1)μ_(g) ^(−R2)=Xf(c₁ ^(c)V^(R1)μ_(g) ^(R2), c₂ ^(c)W^(R1))Y^(−R1)μ_(g) ^(−R2)=Xf(c₁, c₂)^(c)f(V, W)^(R1)f(μ_(g), e_(g))^(R2)Y^(−R1)=Xf(c₁, c₂)^(c)Y^(R1)μ_(g) ^(R2)Y^(−R1)μ_(g) ^(−R2)=f(c₁, c₂)^(c)X. That is, zY^(−R1)μ_(g) ^(−R2) is an output of a randomizable sampler having an error X for f(x). Note that e_(g) is an identity element of the group G.

The expansion of formula given above uses the properties such that X=B(V^(R1′)μ_(g) ^(R2′), W^(R1′))f(V^(R1′)μ_(g) ^(R2′), W^(R1′))⁻¹=B(c₁ ^(c)V^(R1)μ_(g) ^(R2), c₂ ^(c)W^(R1))f(c₁ ^(c)V^(R1)μ_(g) ^(R2), c₂ ^(c)W^(R1)) and that B(c₁ ^(c)V^(R1)μ_(g) ^(R2), c₂ ^(c)W^(R1))=Xf(c₁ ^(c)V^(R1)μ_(g) ^(R2), c₂ ^(c)W^(R1)). The properties are based on the fact that R₁, R₂, R₁′ and R₂′ are random numbers.

Therefore, considering that a(ι) and b(ι) are natural numbers and r(ι, 4), r(ι, 5), r(ι, 6) and r(ι, 7) are random numbers, z_(ι,1)Y_(ι) ^(−r(ι,4))μ_(ι,g) ^(−r(ι,5)) and z_(ι,2)Y_(ι) ^(−r(ι,6))μ_(ι,g) ^(−r(ι,7)) are outputs of randomizable samplers having errors X_(ι,1) and X_(ι,2), respectively, for f_(ι)(c_(ι,1), c_(ι,2)).

Eleventh Embodiment

In the seventh to tenth embodiments described above, a plurality of pairs (a(ι), b(ι)) of natural numbers a(ι) and b(ι) that are relatively prime to each other are stored in the natural number storage 11101 of the computing apparatus and these pairs (a(ι), b(ι)) are used to perform processes. However, one of a(ι) and b(ι) may be a constant. For example, a(ι) may be fixed at 1 or b(ι) may be fixed at 1. Which of natural number a(ι) and b(ι) is a constant may be varied depending on ι. In other words, one of the first randomizable sampler and the second randomizable sampler may be replaced with a sampler. If one of a(ι) and b(ι) is a constant, the need for the process for selecting the constant a(ι) or b(ι) is eliminated, the constant a(ι) or b(ι) is not input in the processing units and the processing units can treat it as a constant to perform computations. If a(ι) or b(ι) set as a constant is equal to 1, f_(ι)(λ_(ι))=u_(ι) ^(b′(ι))v_(ι) ^(a′(ι)) can be obtained as f_(ι)(λ_(ι))=v_(ι) or f_(ι)(λ_(ι))=u_(ι) without using a′(ι) or b′(ι).

An eleventh embodiment is an example of such a variation, in which b(ι) is fixed at 1 and the second randomizable sampler is replaced with a sampler. The following description will focus on differences from the seventh embodiment and description of commonalities with the seventh embodiment, including the decryption control process, will be omitted. Specific examples of the first randomizable sampler and the sampler are similar to those described in the eighth to tenth embodiments and therefore description of the first randomizable sampler and the sampler will be omitted.

<Configuration>

As illustrated in FIG. 18, a proxy computing system 105 of the eleventh embodiment includes a computing apparatus 151 in place of the computing apparatus 111 of the seventh embodiment and capability providing apparatuses 152-1, . . . , 152-Γ in place of the capability providing apparatuses 112-1, . . . , 112-Γ.

As illustrated in FIG. 29, the computing apparatus 151 of the eleventh embodiment includes, for example, a natural number storage 15101, a natural number selecting unit 15102, an input information providing unit 15104, a first computing unit 15105, a first power computing unit 11106, a first list storage 11107, a second computing unit 15108, a second list storage 15110, a determining unit 15111, a final output unit 15112, and a controller 11113.

As illustrated in FIG. 20, the capability providing apparatus 152-ι of the eleventh embodiment includes, for example, a first output information computing unit 15201-ι, a second output information computing unit 15202-ι, a key storage 11204-ι, and a controller 11205-ι. If there are capability providing apparatuses 152-(ι+1), . . . , 52-Γ, the capability providing apparatuses 152-(ι+1), . . . , 52-Γ have the same configuration as the capability providing apparatus 152-ι.

<Decryption Process>

A decryption process of this embodiment will be described below. For the decryption process, let G₁, and H, be groups (for example commutative groups), f_(ι)(λ_(ι)) be a decryption function for decrypting a ciphertext λ_(ι), which is an element of the group H_(ι), with a particular decryption key s_(ι) to obtain an element of the group G_(ι), generators of the groups G_(ι) and H_(ι) be and μ_(ι,g) and μ_(ι,h), respectively, X_(ι,1) and X_(ι,2) be random variables having values in the group G_(ι), x_(ι,1) be a realization of the random variable X_(ι,1), and x_(ι,2) be a realization of the random variable X_(ι,2). It is assumed here that a plurality of natural numbers a(ι) are stored in the natural number storage 15101 of the computing apparatus 151.

As illustrated in FIG. 30, first, the natural number selecting unit 15102 of the computing apparatus 151 (FIG. 29) randomly reads one natural number a(ι) from among the plurality of natural numbers a(ι) stored in the natural number storage 15101. Information on the read natural number a(ι) is sent to the input information providing unit 15104 and the first power computing unit 11106 (step S15100).

The Controller 11113 Sets t₁=1 (Step S11102).

The input information providing unit 15104 generates and outputs first input information τ_(ι,1) and second input information τ_(ι,2) each of which corresponds to the input ciphertext λ_(ι). Preferably, the first input information τ_(ι,1) and the second input information τ_(ι,2) are information whose relation with the ciphertext λ_(ι) is scrambled. This enables the computing apparatus 151 to conceal the ciphertext λ_(ι) from the capability providing apparatus 152-ι. Preferably, the second input information τ_(ι,2) of this embodiment further corresponds to the natural number a(ι) selected by the natural number selecting unit 15102. This enables the computing apparatus 151 to evaluate the decryption capability provided by the capability providing apparatus 152-ι with a high degree of accuracy (step S15103). A specific example of the pair of the first input information τ_(ι,1) and the second input information τ_(ι,2) is a pair of first input information τ_(ι,1) and the second input information τ_(ι,2) of any of the eighth to tenth embodiments when b(ι)=1.

As illustrated in FIG. 26, the first input information τ_(ι,1) is input in the first output information computing unit 15201-ι of the capability providing apparatus 152-ι (FIG. 20) and the second input information τ_(ι,2) is input in the second output information computing unit 15202-ι (step S15200).

The first output information computing unit 15201-ι uses the first input information τ_(ι,1) and the decryption key s_(ι) stored in the key storage 11204-ι to correctly compute f_(ι)(τ_(ι,1)) with a probability greater than a certain probability and sets the result of the computation as first output information z_(ι,1) (step S15201). The second output information computing unit 15202-ι uses the second input information τ_(ι,2) and the decryption key s_(ι) stored in the key storage 11204-ι to correctly compute f_(ι)(τ_(ι,2)) with a probability greater than a certain probability and sets the result of the computation as second output information z_(ι,2) (step S15202). That is, the first output information computing unit 15201-ι and the second output information computing unit 15202-ι output computation results that have an intentional or unintentional error. In other words, the result of the computation by the first output information computing unit 15201-ι may or may not be f_(ι)(τ_(ι,1)) and the result of the computation by the second output information computing unit 15202-ι may or may not be f_(ι)(τ_(ι,2)). A specific example of the pair of the first output information z_(ι,1) and the second output information z_(ι,2) is a pair of first output information z_(ι,1) and the second output information z_(ι,2) of any of the eighth to tenth embodiments when b(ι)=1.

The first output information computing unit 15201-ι outputs the first output information z_(ι,1) and the second output information computing unit 15202-ι outputs the second output information z_(ι,2) (step S15203).

Returning to FIG. 30, the first output information z_(ι,1) is input in the first computing unit 15105 of the computing apparatus 151 (FIG. 29) and the second output information z_(ι,2) is input in the second computing unit 15108. The first output information z_(ι,1) and the second output information z_(ι,2) are equivalent to the decryption capability provided by the capability providing apparatus 152-ι to the computing apparatus 151 (step S15104).

The first computing unit 15105 generates a computation result u_(ι)=f_(ι)(λ_(ι))x_(ι,1) from the first output information z_(ι,1). A specific example of the computation result u_(ι) is a result u_(ι) of computation of any of the eighth to tenth embodiments when b(ι)=1. The computation result u_(ι) is sent to the first power computing unit 11106 (step S15105).

The first power computing unit 11106 computes u_(ι)′=u_(ι) ^(a(ι)). The pair of the result u_(ι) of the computation and u_(ι)′ computed on the basis of the result of the computation, (u_(ι), u_(ι)′), is stored in the first list storage 11107 (step S11106).

The second computing unit 15108 generates a computation result v_(ι)=f_(ι)(λ_(ι))^(a(ι))x_(ι,2) from the second output information z_(ι,2). A specific example of the result v_(ι) of the computation is a result v_(ι) of the computation in any of the eighth to tenth embodiments. The result v_(ι) of the computation is stored in the second list storage 15110 (step S15108).

The determining unit 15111 determines whether or not there is one that satisfies u_(ι)′=v_(ι) among the pairs (u_(ι), u_(ι)′) stored in the first list storage 11107 and v_(ι) stored in the second list storage 15110 (step S15110). If there is one that satisfies u_(ι)′=v_(ι), the process proceeds to step S15114; if there is not one that satisfies u_(ι)′=v_(ι), the process proceeds to step S11111.

At step S11111, the controller 11113 determines whether or not t_(ι)=T_(ι)(step S11111). Here, T_(ι) is a predetermined natural number. If t_(ι)=T_(ι), the final output unit 15112 outputs information indicating that the computation is impossible, for example, the symbol “⊥” (step S11113), then the process ends. If not t_(ι)=T_(ι), the controller 11113 increments t_(ι) by 1, that is, sets t_(ι)=t_(ι)+1 (step S11112), then the process returns to step S15103.

At step S15114, the final output unit 15112 outputs u, corresponding to u_(ι)′ determined to satisfy u_(ι)′=v_(ι)(step S15114). The obtained u_(ι)is equivalent to u_(ι) ^(b′(ι))v_(ι) ^(a′(ι)) in the seventh to tenth embodiments when b(ι)=1. That is, u_(ι) thus obtained can be the decryption result f_(ι)(λ_(ι)) resulting from decrypting the ciphertext λ_(ι) with the particular decryption key s_(ι) with a high probability. Therefore, the process described above is repeated multiple times and the value that has most frequently obtained among the values obtained at step S15114 can be chosen as the decryption result f_(ι)(λ_(ι)). As will be described later, u_(ι)=f_(ι)(λ_(ι)) can result with an overwhelming probability, depending on settings. In that case, the value obtained at step S15114 can be directly provided as a result of decryption f_(ι)(λ_(ι)). The rest of the process is as described in the seventh embodiment.

<<Reason why Decryption Result f_(ι)(λ_(ι)) can be Obtained>>

The reason why a decryption result f_(ι)(λ_(ι)) can be obtained on the computing apparatus 151 of this embodiment will be described below. For simplicity of notation, ι will be omitted in the following description. Terms required for the description will be defined first.

Black-Box:

A black-box F(τ) of f(τ) is a processing unit that takes an input of τϵH and outputs zϵG. In this embodiment, each of the first output information computing unit 15201 and the second output information computing unit 15202 is equivalent to the black box F(τ) for the decryption function f(ι). A black-box F(τ) that satisfies z=f(ι) for an element τϵ_(U)H arbitrarily selected from a group H and z=F(τ) with a probability greater than δ (0<δ≤1), that is, a black-box F(τ) for f(τ) that satisfies Pr[z=f(ι)|τϵ_(U) H,z=F(τ)]>δ  (8) is called a δ-reliable black-box F(τ) for f(ι). Here, δ is a positive value and is equivalent to the “certain probability” stated above.

Self-Corrector

A self-corrector C^(F)(λ) is a processing unit that takes an input of λϵH, performs computation by using a black-box F(ι) for f(ι), and outputs jϵG∪⊥. In this embodiment, the computing apparatus 151 is equivalent to the self-corrector C^(F)(λ).

Almost Self-Corrector:

Assume that a self-corrector C^(F)(λ) that takes an input of λϵH and uses a δ-reliable black-box F(τ) for f(τ) to perform computation outputs a correct value j=f(λ) with a probability sufficiently greater than the provability with which the self-corrector C^(F)(λ) outputs an incorrect value j≠f(λ).

That is, assume that a self-corrector C^(F)(λ) satisfies Pr[j=f(λ)|j=C ^(F)(λ),j≠⊥]>Pr[j≠f(λ)|j=C ^(F)(λ),j≠⊥]+Δ   (9) Here, Δ is a certain positive value (0<Δ<1). If this is the case, the self-corrector C^(F)(λ) is called an almost self-corrector. For example, for a certain positive value Δ′ (0<Δ′<1), if a self-corrector C^(F)(λ) satisfies Pr[j=f(λ)|j=C ^(F)(λ)]>(⅓)+Δ′ Pr[j=⊥|j=C ^(F)(λ)]<⅓ Pr[j≠f(λ) and j≠⊥|j=C ^(F)(λ)]<⅓, then the self-corrector C^(F)(λ) is an almost self-corrector. Examples of Δ′ include Δ′= 1/12 and Δ′=⅓.

Robust Self-Corrector:

Assume that a self-corrector C^(F)(λ) that takes an input of λϵH and uses a δ-reliable black-box F(τ) for f(τ) outputs a correct value j=f(λ) or j=⊥ with an overwhelming probability. That is, assume that for a negligible error ξ (0≤ξ<1), a self-corrector C^(F)(λ) satisfies Pr[j=f(λ) or j=⊥|j=C ^(F)(λ)]>1−ξ  (10) If this is the case, the self-corrector C^(F)(λ) is called a robust self-corrector. An example of the negligible error ξ is a function vale ξ(k) of a security parameter k. An example of the function value ξ(k) is a function value ξ(k) such that {ξ(k)p(k)} converges to 0 for a sufficiently large k, where p(k) is an arbitrary polynomial. Specific examples of the function value ξ(k) include ξ(k)=2^(−k) and ξ(k)=2^(−√k).

A robust self-corrector can be constructed from an almost self-corrector. Specifically, a robust self-corrector can be constructed by executing an almost self-corrector multiple times for the same λ, and selecting the most frequently output value, except ⊥, as j. For example, an almost self-corrector is executed O(log(1/ξ)) times for the same λ and the value most frequently output is selected as j, thereby a robust self-corrector can be constructed. Here, O(⋅) represents O notation.

Pseudo-Free Action:

An upper bound of the probability Pr[α ^(a)=β and α≠e _(g) |aϵ _(U) Ω,αϵX ₁ ,βϵX ₂]  (11) of satisfying α^(a)=β for all possible X₁ and X₂ is called a pseudo-free indicator of a pair (G, Ω) and is represented as P(G, Ω), where G is a group, Ω is a set of natural numbers Ω={0, . . . , M} (M is a natural number greater than or equal to 1), α and β are realizations αϵX₁ (α≠e_(g)) and (βϵX₂ of random variables X₁ and X₂ that have values in the group G, and aϵΩ. If a certain negligible function ζ(k) exists and P(G,Ω)<ζ(k)  (12), then a computation defined by the pair (G, Ω) is called a pseudo-free action. Note that “α^(a)” means that a computation defined at the group G is applied a times to α. An example of the negligible function ζ(k) is such that {ζ(k)p(k)} converges to 0 for a sufficiently large k, where p(k) is an arbitrary polynomial. Specific examples of the function ζ(k) include ζ(k)=2^(−k) and ζ(k)=2^(−√k). For example, if the probability of Formula (11) is less than O(2^(−k)) for a security parameter k, a computation defined by the pair (G, Ω) is a pseudo-free action. For example, if the number of the elements |Ω·α| of a set Ω·α={a(α)|aϵΩ} exceeds 2^(k) for any ∀αϵG where α≠e_(g), a computation defined by the pair (G, Ω) is a pseudo-free action. Note that a(α) represents the result of a given computation on a and α. There are many such examples. For example, if the group G is a residue group Z/pZ modulo prime p, the prime p is the order of 2^(k), the set Ω={0, . . . p−2}, a(α) is α^(a)ϵZ/pZ, and α≠e_(g), then Ω·α={α^(a)|a=0, . . . p−2}={e_(g), α¹, . . . , α^(p-2)} and |Ω·α|=p−1. If a certain constant C exists and k is sufficiently large, |Ω·α|>C2^(k) is satisfied because the prime p is the order of 2^(k). Here, the probability of Formula (11) is less than C⁻¹2^(−k) and a computation defined by such pair (G, Ω) is a pseudo-free action.

δ^(γ)-Reliable Randomizable Sampler:

A randomizable sampler that whenever a natural number a is given, uses the δ-reliable black-box F(τ) for f(ι) and returns w^(a)x′ corresponding to a sample x′ that depends on a random variable X for wϵG and in which the probability that w^(a)x′=w^(a) is greater than δ^(γ) (γ is a positive constant), that is, Pr[w ^(a) x′=w ^(a)]>δγ  (13) is satisfied, is called a δ^(γ)-reliable randomizable sampler. The combination of the input information providing unit 15104, the second output information computing unit 15202, and the second computing unit 15108 of this embodiment is a δ^(γ)-reliable randomizable sampler for w=f(λ).

The definitions given above will be used to describe the reason why a decryption result f(λ) can be obtained by using the computing apparatus 151 of this embodiment.

At step S15110 of this embodiment, determination is made as to whether u′=v_(ι) that is, whether u^(a)=v. Since the combination of the input information providing unit 15104, the second output information computing unit 15202, and the second computing unit 15108 of this embodiment is a δ^(γ)-reliable randomizable sampler (Formula (13)), u^(a)=v holds (Yes at step S15110) with an asymptotically large probability if T is greater than a certain value determined by k, δ and γ. For example, Markov's inequality shows that if T≥4/δ^(γ), the probability that u^(a)=v holds (Yes at step S15110) is greater than ½.

Since u=f(λ)x₁ and v=f(λ)^(a)x₂ in this embodiment, x₁ ^(a)=x₂ holds if u^(a)=v holds. x₁ ^(a)=x₂ holds if x₁=x₂=e_(g) or x≠e_(g). If x₁=x₂=e_(g), then u=f(λ) and therefore u output at step S15114 is a correct decryption result f(λ). On the other hand, if x₁≠e_(g), then u≠f(λ) and therefore u output at step S15114 is not a correct decryption result f(λ).

If an computation defined by a pair (G, Ω) of a group G and a set Ω to which a natural number a belongs is a pseudo-free action or T²P(G, Ω) is asymptotically small for a pseudo-free index P(G, Ω), the probability that x₁≠e_(g) (Formula (11)) when u^(a)=v is asymptotically small. Accordingly, the probability that x₁=e_(g) when u^(a)=v is asymptotically large. Therefore, if a computation defined by a pair (G, Ω) is a pseudo-free action or T²P(G, Ω) is asymptotically small, the probability that an incorrect decryption result f(λ) is output when u^(a)=v is sufficiently smaller than the probability that a correct decryption result f(λ) is output when u^(a)=v. In this case, it can be said that the computing apparatus 151 is an almost self-corrector (see Formula (9)). Therefore, a robust self-corrector can be constructed from the computing apparatus 151 as described above and a correct decryption result f(λ) can be obtained with an overwhelming probability. If a computation defined by (G, Ω) is a pseudo-free action, the probability that an incorrect decryption result f(λ) is output when u^(a)=v is also negligible. In that case, the computing apparatus 151 outputs a correct decryption result f(λ) or ⊥ with an overwhelming probability.

Note that “η(k′) is asymptotically small” means that k₀ is determined for an arbitrary constant ρ and the function value η(k′) for any k′ that satisfies k₀<k′ for k₀ is less than ρ. An example of k′ is a security parameter k.

“η(λ) is asymptotically large” means that k₀ is determined for an arbitrary constant ρ and the function value 1−η(k′) for any k′ that satisfies k₀<k′ for k₀ is less than ρ.

Note that the proof given above also proves that “if u′=v′ holds, it is highly probable that the first randomizable sampler has correctly computed u=f(λ)^(b) and the second randomizable sampler has correctly computed v=f(λ)^(a) (x₁ and x₂ are identity elements e_(g) of the group G)” stated in the seventh embodiment, as can be seen by replacing a with a/b.

<<δ^(γ)-Reliable Randomizable Sampler and Security>>

Consider the following attack.

-   -   A black-box F(τ) or part of the black-box F(τ) intentionally         outputs an invalid z or a value output from the black-box F(τ)         is changed to an invalid z.     -   w^(a)x′ corresponding to the invalid z is output from the         randomizable sampler.     -   w^(a)x′ corresponding to the invalid z increases the probability         with which the self-corrector C^(F)(λ) outputs an incorrect         value even though u^(a)=v holds (Yes at step S15110) in the         self-corrector C^(F)(λ).

Such an attack is possible if the probability distribution D_(a)=w^(a)x′w^(−a) of an error of w^(a)x′ output from the randomizable sampler for a given natural number a depends on the natural number a. For example, if tampering is made so that v output from the second computing unit 15108 is f(λ)^(a)x₁ ^(a), always u^(a)=v holds regardless of the value of x₁. Therefore, it is desirable that the probability distribution D_(a)=w^(a)x′w^(−a) of an error of w^(a)x′ output from the randomizable sampler for a given natural number a do not depend on the natural number a.

Alternatively, it is desirable that the randomizable sampler be such that a probability distribution D that has a value in a group G that cannot be distinguished from the probability distribution D_(a)=w^(a)x′w^(−a) of an error of w^(a)x′ for any element aϵ∀Ω of a set Ω exists (the probability distribution D_(a) and the probability distribution D are statistically close to each other). Note that the probability distribution D does not depend on a natural number a. That the probability distribution D_(a) and the probability distribution D cannot be distinguished from each other means that the probability distribution D_(a) and the probability distribution D cannot be distinguished from each other by a polynomial time algorithm. For example, if Σ_(gϵG) |Pr[gϵD]−Pr[gϵD _(a)]|<ζ  (14) is satisfied for negligible ζ (0≤ζ1), the probability distribution D_(a) and the probability distribution D cannot be distinguished from each other by the polynomial time algorithm. An example of negligible ζ is a function value ζ(k) of the security parameter k. An example of the function value ζ(k) is a function value such that {ζ(k)p(k)} converges to 0 for a sufficiently large k, where p(k) is an arbitrary polynomial. Specific examples of the function ζ(k) include ζ(k)=2^(−k) and ζ(k)=2^(−√k). These facts also apply to the seventh to tenth embodiments which use natural numbers a and b.

[Variations of Seventh to Eleventh Embodiment]

In the seventh to eleventh embodiments, when the first output information z_(ι,1) and the second output information z_(ι,2) are provided to the computing apparatus, the computing apparatus can output u_(ι) ^(b′(ι))v_(ι) ^(a′(ι)) with a certain probability. u_(ι) ^(b′(ι))v_(ι) ^(a′(ι)) will be the decryption value of the ciphertext λ_(ι). On the other hand, if neither of the first output information z_(ι,1) and the second output information z_(ι,2) is provided to the computing apparatus, the computing apparatus cannot obtain the decryption value of the ciphertext λ_(ι).

The capability providing apparatus in any of the seventh to eleventh embodiment can control whether or not to output both of the first output information z_(ι,1) and second output information z_(ι,2) to control the ciphertext decryption capability of the computing apparatus without providing a decryption key to the computing apparatus.

The present invention is not limited to the embodiments described above. For example, while ω is an integer greater than or equal to 2 in the embodiments described above, ω may be 1. That is, a configuration in which only one capability providing apparatus exists may be used. In that case, the computing apparatus does not need to include a recovering unit and may directly output a value output from the final output unit. For example, the system of any of the first to fifth embodiments described above may further include a decryption control apparatus, the decryption control apparatus may output a decryption control instruction for controlling the decryption process of the computing apparatus to the capability providing apparatus, and the capability providing apparatus may control whether or not to output both of first output information z₁ and second output information z₂ from the first output information computing unit and the second output information computing unit according to the decryption control instruction. For example, the system of the sixth embodiment described above may further include a decryption control apparatus, the decryption control apparatus may output a decryption control instruction for controlling the decryption process of the computing apparatus to the capability providing apparatus, and the capability providing apparatus may control whether or not to output first output information _(M)z₁ and second output information _(M)z₂ from the first output information computing unit and the second output information computing unit according to the decryption control instruction.

Random variables X_(ι,1), X_(ι,2) and X_(ι,3) may or may not be the same.

Each of the first random number generator, the second random number generator, the third random number generator, the fourth random number generator, the fifth random number generator, the sixth random number generator and the seventh random number generator generates uniform random numbers to increase the security of the proxy computing system. However, if the level of security required is not so high, at least some of the first random number generator, second random number generator, the third random number generator, the fourth random number generator, the fifth random number generator, the sixth random number generator and the seventh random number generator may generate random numbers that are not uniform random numbers. While it is desirable from the computational efficiency point of view that random numbers that are natural numbers greater than or equal to 0 and less than K_(ι,H) or random numbers that are natural numbers greater than or equal to 0 and less than K_(ι,G) be selected as in the embodiments described above, random numbers that are natural numbers greater than or equal to K_(ι,H) or K_(ι,G) may be selected instead.

The process of the capability providing apparatus may be performed multiple times each time the computing apparatus provides first input information τ_(ι,1) and second input information τ_(ι,2) corresponding to the same a(ι) and b(ι) to the capability providing apparatus. This enables the computing apparatus to obtain a plurality of pieces of first output information z_(ι,1), second output information z_(ι,2), and third output information z_(ι,3) each time the computing apparatus provides first input information τ_(ι,1) and the second input information τ_(ι,2) to the capability providing apparatus. Consequently, the number of exchanges and the amount of communication between the computing apparatus and the capability providing apparatus can be reduced.

The computing apparatus may provide a plurality of pieces of the first input information τ_(ι,1) and the second input information τ_(ι,2) to the capability providing apparatus at once and may obtain a plurality of pieces of corresponding first output information z_(ι,1), second output information z_(ι,2) and third output information z_(ι,1) at once. This can reduce the number of exchanges between the computing apparatus and the capability providing apparatus.

While ω in the embodiments is a constant, ω may be a variable, provided that a value of ω can be shared in the proxy computing system.

The units of the computing apparatus may exchange data directly or through a memory, which is not depicted. Similarly, the units of the capability providing apparatus may exchange data directly or through a memory, which is not depicted.

Check may be made to see whether u_(ι) and v_(ι) obtained at the first computing unit and the second computing unit of any of the embodiments are elements of the group G_(ι). If they are elements of the group G_(ι), the process described above may be continued; if u_(ι) or v_(ι) is not an element of the group G_(ι), information indicating that the computation is impossible, for example the symbol “⊥”, may be output.

Furthermore, the processes described above may be performed not only in time sequence as is written or may be performed in parallel with one another or individually, depending on the throughput of the apparatuses that perform the processes or requirements. A plurality of capability providing apparatus may be configured in a single apparatus. It would be understood that other modifications can be made without departing from the spirit of the present invention.

Twelfth Embodiment

A twelfth embodiment of the present invention will be described. In this embodiment, Φ computing apparatuses (Φ is an integer greater than or equal to 2) shares one capability providing apparatus to perform computations and the capability providing apparatus receives payment for the capability. However, this does not limit the present invention and Φ computing apparatuses may share a plurality of capability providing apparatuses to perform computations.

<Configuration>

As illustrated in FIG. 31, a proxy computing system 201 of the twelfth embodiment includes, for example, Φ computing apparatuses 211-φ (φ=1, . . . , Φ) and one capability providing apparatus 212. The apparatuses are configured to be able to exchange data between them. For example, the apparatuses can exchange information through a transmission line, a network, a portable recording medium and other media.

In this embodiment, the capability providing apparatus 212 provides the capability of computing a function f_(φ) that maps elements of a group H_(φ) to elements of a group G_(φ) (the computing capability) to each computing apparatus 211-φ. Each computing apparatus 211-φ pays the capability providing apparatus 212 for the capability. The computing apparatus 211-φ uses the provided capability to compute an element f_(φ)(x_(φ)) of the group G_(φ) that corresponds to an element x_(φ) of the group H_(φ).

As illustrated in FIG. 32, the computing apparatus 211-φ of the twelfth embodiment includes, for example, a natural number storage 21101-φ, a natural number selecting unit 21102-φ, an integer computing unit 21103-φ, an input information providing unit 21104-φ, a first computing unit 21105-φ, a first power computing unit 21106-φ, a first list storage 21107-φ, a second computing unit 21108-φ, a second power computing unit 21109-φ, a second list storage 21110-φ, a determining unit 21111-φ, a final output unit 21112-φ, and a controller 21113-φ. The computing apparatus 211-φ performs processes under the control of the controller 21113-φ. Examples of the computing apparatuses 211-φ include devices having computing and memory functions, such as well-known or specialized computers, that include a CPU (central processing unit) and a RAM (random-access memory) in which a special program is loaded, server devices, a gateway devices, card reader-writer apparatuses and mobile phones.

As illustrated in FIG. 33, the capability providing apparatus 212 of the twelfth embodiment includes, for example, a first output information computing unit 21201, a second output information computing unit 21202, and a controller 21205. The capability providing apparatus 212 performs processes under the control of the controller 21205. Examples of the capability providing apparatus 212 include a well-known or specialized computer, a device including computing and memory functions, such as a mobile phone, and a tamper-resistant module such as an IC card and an IC chip, that include a CPU and a RAM in which a special program is loaded.

<Assumptions for Processes>

Let G_(φ), H_(φ) be groups (for example commutative groups), X_(φ,1) and X_(φ,2) be random variables having values in the group G_(φ), x_(φ,1) be a realization of the random variable X_(φ,1), x_(φ,2) be a realization of the random variable X_(φ,2), f_(φ) be a function that maps an element of the group H_(φ) to an element of the group G_(φ), and a(φ) and b(φ) be natural numbers that are relatively prime to each other. Specific examples of f_(φ) include an encryption function, a decryption function, a re-encryption function, an image processing function, and a speech processing function. G_(φ) may be equal to H_(φ) or G_(φ) may be unequal to H_(φ). All of the groups G_(φ) (φ=1, . . . , Φ) may be the same or at least some groups G_(φ′) may differ from the other groups G_(φ), where φ′≠φ. All of the groups H_(φ)(φ=1, . . . , Φ) may be the same or at some groups H_(φ′) may differ from the other groups H_(φ), where φ′≠φ. In the following description, computations on the groups G_(φ) and H_(φ) are multiplicatively expressed. a(φ) and b(φ) are natural numbers that are relatively prime to each other. The term “natural number” refers to an integer greater than or equal to 0. A set whose members are elements f_(φ)(M_(φ))^(a(φ)b(φ)) of the group G_(φ) that correspond to elements M_(φ) of the group H_(φ) is referred to as a “class CL_(φ)(M_(φ)) corresponding to elements M_(φ)”. Here, CL_(φ)(M_(φ)) and CL_(φ′)(M_(φ′)) (φ′≠φ) are classes different from each other. If a function f_(φ)(M_(φ)) is an injective function for an element M_(φ), only one member belongs to the class CL_(φ)(M_(φ)) corresponding to the same element M_(φ) for a pair of a(φ) and b(φ). If only one member belongs to the class CL_(φ)(M_(φ)) corresponding to the same element M_(φ) for each pair of a(φ) and b(φ), that two values belong to the class CL_(φ)(M_(φ)) corresponding to the same M_(φ) is equivalent to that the two values are equal to each other. That is, if the function f_(φ)(M_(φ)) is a injective function for the element M_(φ), determination as to whether two values belong to the class CL_(φ)(M_(φ)) corresponding to the same element M_(φ) or not can be made by determining whether the two values are equal to each other or not. On the other hand, if the function f_(φ)(M_(φ)) is not an injective function for the element M_(φ) (for example if the function f_(φ) is an encryption function of probabilistic encryption such as ElGamal encryption), a plurality of members belong to the class CL_(φ)(M_(φ)) corresponding to the same element M_(φ) for the pair of a(φ) and b(φ) because a plurality of ciphertexts correspond to a pair of a plaintext and an encryption key.

It is assumed here that a plurality of pairs (a(φ), b(φ)) of natural numbers a(φ) and b(φ) are stored in the natural number storage 21101-φ of each computing apparatus 211-φ (FIG. 32). Let I_(φ) be a set of pairs of relatively prime natural numbers that are less than the order of the group G_(φ). Then it can be considered that pairs (a(φ), b(φ)) of natural numbers a(φ) and b(φ) corresponding to a subset S_(φ) of I_(φ) are stored in the natural number storage 21101-φ.

<Processes>

Processes performed by the computing apparatus 211-φ by using the capability providing apparatus 212 will be described. The processes may be performed by any one of the computing apparatuses 211-φ occupying the capability providing apparatus 212 for a certain period of time or may be performed concurrently by a plurality of computing apparatuses 211-φ accessing the capability providing apparatus 212.

An element x_(φ) of the group H_(φ) is input in the input information providing unit 21104-φ of a computing apparatus 211-φ (FIG. 32). If the element x_(φ) has been already input in the input information providing unit 21104-φ, the input operation may be omitted.

As illustrated in FIG. 37, the natural number selecting unit 21102-φ of the computing apparatus 211-φ in which the element x_(φ) is input in the input information providing unit 21104-φ randomly reads one pair of natural numbers (a(φ), b(φ)) from among the plurality of pairs of natural numbers (a(φ), b(φ)) stored in the natural number storage 21101-φ. At least some of information on the read pair of natural numbers (a(φ), b(φ)) is sent to the integer computing unit 21103-φ, the input information providing unit 21104-φ, the first power computing unit 21106-φ and the second power computing unit 21109-φ (step S21100).

The integer computing unit 21103-φ uses the sent pair of natural numbers (a(φ), b(φ)) to computer integers a′(φ) and b′(φ) that satisfy the relation a′(φ)a(φ)+b′(φ)^(b)(φ)=1. Since the natural numbers a(φ) and (φ) are relatively prime to each other, the integers a′(φ) and b′(φ) that satisfy the relation a′(φ)^(a)(φ)+b′(φ)^(b)(φ)=1 definitely exist. Methods for computing such integers are well known. For example, a well-known algorithm such as the extended Euclidean algorithm may be used to compute the integers a′(φ) and b′(φ). Information on the pair of natural numbers (a′(φ), b′(φ)) is sent to the final output unit 21112-φ (step S21101).

The Controller 21113-φ Sets t=1 (Step S21102).

The input information providing unit 21104-φ generates and outputs first input information τ_(φ,1) and second input information τ_(φ,2) each of which corresponds to the input element x_(φ). Preferably, the first input information τ_(φ,1) and the second input information τ_(φ,2) are information whose relation with the element x_(φ) is scrambled. This enables the computing apparatus 211-φ to conceal the element x_(φ) from the capability providing apparatus 212. Preferably, the first input information τ_(φ,1) of this embodiment further corresponds to the natural number b(φ) selected by the natural number selecting unit 21102-φ and the second input information τ_(φ,2) further corresponds to the natural number a(φ) selected by the natural number selecting unit 21102-φ. This enables the computing apparatus 211-φ to evaluate the computation capability provided by the capability providing apparatus 212 with a high degree of accuracy (step S21103).

As illustrated in FIG. 38, the first input information τ_(φ,1) is input in the first output information computing unit 21201 of the capability providing apparatus 212 (FIG. 33) and the second input information τ_(φ,2) is input in the second output information computing unit 21202 (step S21200).

The first output information computing unit 21201 uses the first input information τ_(φ,1) to correctly compute f_(φ)(τ_(φ,1)) with a probability greater than a certain probability and sets the obtained result of the computation as first output information z_(φ,1) (step S21201). The second output information computing unit 21202 uses the second input information τ_(φ,2) to correctly computes f_(φ)(ι_(φ,2)) with a probability greater than a certain probability and sets the obtained result of the computation as second output information z_(φ,2) (step S21202). Note that the “certain probability” is a probability less than 100%. An example of the “certain probability” is a nonnegligible probability and an example of the “nonnegligible probability” is a probability greater than or equal to 1/ψ(k), where ψ(k) is a polynomial that is a weakly increasing function (non-decreasing function) for a security parameter k. That is, the first output information computing unit 21201 and the second output information computing unit 21202 can output computation results that have an intentional or unintentional error. In other words, the result of the computation by the first output information computing unit 21201 may or may not be f_(φ)(τ_(φ,1)) and the result of the computation by the second output information computing unit 21202 may or may not be f_(φ)(τ_(φ,2)). The first output information computing unit 21201 outputs the first output information z_(φ,1) and the second output information computing unit 21202 outputs the second output information z_(φ,2) (step S21203).

Returning to FIG. 37, the first output information z_(φ,1) is input in the first computing unit 21105-φ of the computing apparatus 211-φ (FIG. 32) and the second output information z_(φ,2) is input in the second computing unit 21108-φ. The first output information z_(φ,1) and the second output information z_(φ,2) are equivalent to the computation capability provided by the capability providing apparatus 212 to the computing apparatus 211-φ (step S21104). A user of the computing apparatus 211-φ to which the computation capability has been provided pays the capability providing apparatus 212 for the computation capability. The payment may be made through a well-known electronic payment process, for example.

The first computing unit 21105-φ generates computation result u_(φ) =f_(φ)(x_(φ))^(b(φ))x_(φ,1) from the first output information z_(φ,1). Here, generating (computing) f_(φ)(x_(φ))^(b(φ))x_(φ,1) means computing a value of a formula defined as f_(φ)(x_(φ))^(b(φ))x_(φ,1). Any intermediate computation method may be used that can eventually compute the value of the formula f_(φ)(x_(φ))^(b(φ))x_(φ,1). The same applies to computations of the other formulae that appear herein. The result u_(φ) of the computation is sent to the first power computing unit 21106-φ (step S21105).

The first power computing unit 21106-φ computes u_(φ)′=u_(φ) ^(a(φ)). The pair of the result u_(φ) of the computation and u_(φ)′ computed on the basis of the result of the computation, (u_(φ), u_(φ)′), is stored in the first list storage 21107-φ (step S21106).

The determining unit 21111-φ determines whether or not there is a pair of u_(φ)′ and v_(φ)′ that belong to a class CL_(φ)(M_(φ)) corresponding to the same element M_(φ) among the pairs (u_(φ), u_(φ)′) stored in the first list storage 21107-φ and the pairs (v_(φ), v_(φ)′) stored in the second list storage 21110-φ. Whether M_(φ) =x_(φ) does not need to be determined (The same applies to the following determinations.). In other words, the determining unit 21111-φ determines whether there is a pair of u_(φ)′ and v_(φ)′ that belong to the class CL_(φ)(M_(φ)) corresponding to the same element M_(φ). For example, if the function f_(φ)(M_(φ)) is an injective function for the element M_(φ), the determining unit 21111-φ determines whether or not u_(φ)′=v_(φ)′ (step S21107). If a pair (v_(φ), v_(φ)′) is not stored in the second list storage 21110-φ, the process at step S21107 is omitted and the process at step S21108 is performed. If there is a pair of u_(φ)′ and v_(φ)′ that belong to the class CL_(φ)(M_(φ)) corresponding to the same element M_(φ), the process proceeds to step S21114. If there is not a pair of u_(φ)′ and v_(φ)′ that belong to the class CL_(φ)(M_(φ)) corresponding to the same element M_(φ), the process proceeds to step S21108.

At step S21108, the second computing unit 21108-φ generates a computation result v_(φ)=f_(φ)(x_(φ))^(a(φ)x) _(φ,2) from the second output information z_(φ,2). The result v_(φ) of the computation is sent to the second power computing unit 21109-φ (step S21108).

The second power computing unit 21109-φ computes v_(φ)′=v_(φ) ^(b(φ)). The pair of the result v_(φ) of the computation and v_(φ)′ computed on the basis of the computation result, (v_(φ), v_(φ)′), is stored in the second list storage 21110-φ (step S21109).

The determining unit 21111-φ determines whether or not there is a pair of u_(φ)′ and v_(φ)′ that belong to a class CL_(φ)(M_(φ)) corresponding to the same element M_(φ) among the pairs (u_(φ), u_(φ)′) stored in the first list storage 21107-φ and the pairs (v_(φ), v_(φ)′) stored in the second list storage 21110-φ. For example, if the function f_(φ)(M_(φ)) is an injective function for the element M_(φ), the determining unit 21111-φ determines whether or not u_(φ)′=v_(φ)′ (step S21110). If there is a pair of u_(φ)′ and v_(φ)′ that belong to the class CL_(φ)(M_(φ)) corresponding to the same element M_(φ), the process proceeds to step S21114. If there is not a pair of u_(φ)′ and v_(φ)′ that belong to the class CL_(φ)(M_(φ)) corresponding to the same element M_(φ)), the process proceeds to step S21111.

At step S21111, the controller 21113-φ determines whether or not t=T (step S21111). Here, T is a predetermined natural number. T may have the same value for all of φ or the value of T in the self-correction process for φ and the value of T in the self-correction process for φ′ (φ′≠φ, φ′=1, . . . , Φ) may be different. If t=T, the final output unit 21112-φ outputs information indicating that the computation is impossible, for example the symbol “⊥” (step S21113) and the process ends. If not t=T, the controller 21113-φ increments t by 1, that is, sets t=t+1 (sets t+1 as a new t) (step S21112) and the process returns to step S21103.

The information indicating that the computation is impossible (the symbol “⊥” in this example) means that the reliability that the capability providing apparatus 212 correctly performs computation is lower than a criterion defined by T. In other words, the capability providing apparatus 212 was unable to perform a correct computation in T trials.

At step S21114, the final output unit 21112-φ uses u_(φ) and v_(φ) that correspond to the pair of u_(φ)′ and v_(φ)′ that are determined to belong to the class CL_(φ)(M_(φ)) corresponding to the same element M_(φ) to calculate and output u_(φ) ^(b′(φ))v_(φ) ^(a′(φ)), and then the process ends (step S21114).

The u_(φ) ^(b′(φ))v_(φ) ^(a′(φ)) thus computed is equal to f_(φ)(x_(φ))ϵG_(φ) with a high probability (the reason why u_(φ) ^(b′(φ))v_(φ) ^(a′(φ))=f_(φ)(x_(φ)) with a high probability will be described later). Therefore, u_(φ) ^(b′(φ))v_(φ) ^(a′(φ))=f_(φ)(x_(φ)) results with a given reliability (such as a probability) or greater by repeating at least the process for φ described above multiple times and selecting the value u_(φ) ^(b′(φ))v_(φ) ^(a′(φ)) obtained with the highest frequency among the values obtained at step S21114. As will be described later, u_(φ) ^(b′(φ))v_(φ) ^(a′(φ))=f_(φ)(x_(φ)) can result with an overwhelming probability, depending on settings. This also holds true even if the capability providing apparatus 212 does not necessarily return a correct response. Therefore, the computing apparatus 211-φ does not perform verification for confirming the validity of the capability providing apparatus 212. Even if a process performed between another computing apparatus 2114-φ′ (φ′≠φ) and the capability providing apparatus 212 affects the process performed between the computing apparatus 211-φ and the capability providing apparatus 212, the computing apparatus 211-φ can obtain a correct computation result f_(φ)(x_(φ)) if the capability providing apparatus 212 returns a correct solution with a probability greater than a certain probability.

<<Reason why u_(φ) ^(b′(φ))v_(φ) ^(a′(φ))=f_(φ)(x_(φ)) with High Probability>>

Let X_(φ) be a random variable having a value in the group G_(φ). For w_(φ)ϵG_(φ), an entity that returns w_(φ)x_(φ)′ corresponding to a sample x_(φ)′ according to a random variable X_(φ) in response to each request is called a sampler having an error X_(φ) for w_(φ).

For w_(φ)ϵG_(φ), an entity that returns w_(φ) ^(a(φ))x_(φ)′ corresponding to a sample x_(φ)′ according to a random variable X_(φ) whenever a natural number a(φ) is given is called a randomizable sampler having an error X_(φ) for w_(φ). The randomizable sampler functions as a sampler if used with a(φ)=1.

The combination of the input information providing unit 21104-φ, the first output information computing unit 21201 and the first computing unit 21105-φ of this embodiment is a randomizable sampler having an error X_(φ,1) for f_(φ)(x_(φ)) (referred to as the “first randomizable sampler”) and the combination of the input information providing unit 21104-φ, the second output information computing unit 21202 and the second computing unit 21108-φ is a randomizable sampler having an error X_(φ,2) for f_(φ)(x_(φ)) (referred to as the “second randomizable sampler”).

The inventor has found that if u_(φ)′ and v_(φ)′ belong to the class CL_(φ)(M_(φ)) corresponding to the same element M_(φ), it is highly probable that the first randomizable sampler has correctly computed u_(φ)=f_(φ)(x_(φ))^(b(φ)) and the second randomizable sampler have correctly computed v_(φ)=f_(φ)(x_(φ))^(a(φ)) (x_(φ,1) and x_(φ,2) are identity elements e_(φ,g) of the group G_(φ)). For simplicity of explanation, this will be proven in an eighteenth embodiment.

When the first randomizable sampler correctly computes u_(φ)=f_(φ)(x_(φ))^(b(φ)) and the second randomizable sampler correctly computes v_(φ)=f_(φ)(x_(φ))^(a(φ)) (when x_(φ,1) and x_(φ,2) are identity elements e_(φ,g) of the group G_(φ)), then u_(φ) ^(b′(φ))=(f_(φ)(x_(φ))^(b(φ))x_(φ,1))^(b′(φ))=(f_(φ)(x_(φ))^(b(φ)e) _(φ,g))^(b′(φ)=f) _(φ)(x_(φ))^(b(φ)b′(φ)) and v_(φ) ^(a′(φ))=(f_(φ)(x_(φ))^(a(φ)x) _(φ,2))^(a′(φ))=(f_(φ)(x_(φ))^(a(φ))e_(φ,g))^(a′(φ))=f_(φ)(x_(φ))^(a(φ)a′(φ)). Therefore, if the function f_(φ)(M_(φ)) is an injective function for the element M_(φ), then u_(φ) ^(b′(φ))v_(φ) ^(a′(φ))=f_(φ)(x_(φ) ^((b(φ)b′(φ)+a(φ)a′(φ)))=f_(φ)(x_(φ)). On the other hand, if the function f_(φ)(M_(φ)) is not an injective function for the element M_(φ) but if a homomorphic function, then u_(φ) ^(b′(φ))v_(φ) ^(a′(φ))=f_(φ)(x_(φ)) ^(b(φ)b′(φ)+a(φ)a′(φ)))=f_(φ)(x_(φ)).

For (q₁, q₂)ϵI, a function π_(e) is defined as π_(i)(q₁, q₂)=q_(i) for each of i=1, 2. Let L=min (#π₁(S), #π₂(S)), where #⋅ is the order of a set ⋅. If the group G_(φ) is a cyclic group or a group whose order is difficult to compute, it can be expected that the probability that an output of the computing apparatus 211-φ other than “⊥” is not f_(φ)(x_(φ)) is at most approximately T² L/#S within a negligible error. If L/#S is a negligible quantity and T is a quantity approximately equal to a polynomial order, the computing apparatus 211-φ outputs a correct f_(φ)(x_(φ)) with an overwhelming probability. An example of S that results in a negligible quantity of L/#S is S={(1, d)|dϵ[2,|G_(φ)|−1]}.

Thirteenth Embodiment

A proxy computing system of an thirteenth embodiment is an example that embodies the first randomizable sampler and the second randomizable sampler described above. The following description will focus on differences from the twelfth embodiment and repeated description of commonalities with the twelfth embodiment will be omitted. In the following description, elements labeled with the same reference numerals have the same functions and the steps labeled with the same reference numerals represent the same processes.

<Configuration>

As illustrated in FIG. 31, the proxy computing system 202 of the thirteenth embodiment includes a computing apparatus 221-φ in place of the computing apparatus 211-φ and a capability providing apparatus 222 in place of the capability providing apparatus 212.

As illustrated in FIG. 32, the computing apparatus 221-φ of the thirteenth embodiment includes, for example, a natural number storage 21101-φ, a natural number selecting unit 21102-φ, an integer computing unit 21103-φ, an input information providing unit 22104-φ, a first computing unit 22105-φ, a first power computing unit 21106-φ, a first list storage 21107-φ, a second computing unit 22108-φ, a second power computing unit 21109-φ, a second list storage 21110-φ, a determining unit 21111-φ, a final output unit 21112-φ, and a controller 21113-φ. As illustrated in FIG. 34, the input information providing unit 22104-φ of this embodiment includes, for example, a first random number generator 22104 a-φ, a first input information computing unit 2210413-φ, a second random number generator 22104 c-φ, and a second input information computing unit 22104 d-φ.

As illustrated in FIG. 33, the capability providing unit 222 of the thirteenth embodiment includes, for example, a first output information computing unit 22201, a second output information computing unit 22202, and a controller 21205.

<Assumptions for Processes>

In the thirteenth embodiment, a function f_(φ) is a homomorphic function, a group K_(φ) is a cyclic group, a generator of the group K_(φ) is μ_(φ,h), the order of the group H_(φ) is K_(φ,H), and ν_(φ)=f_(φ)(μ_(φ,h)). The rest of the assumptions are the same as those in the twelfth embodiment, except that the computing apparatus 211-φ is replaced with the computing apparatus 221-φ and the capability providing apparatus 212 is replaced with the capability providing apparatuses 222.

<Process>

As illustrated in FIGS. 37 and 38, the process of the thirteenth embodiment is the same as the process of the twelfth embodiment except that steps S21103 through S21105, S21108, and S21200 through S21203 of the twelfth embodiment are replaced with steps S22103 through S22105, S22108, and S22200 through S22203, respectively. In the following, only processes at steps S22103 through S22105, S22108, and S22200 through S22203 will be described.

<<Process at Step S22103>>

The input information providing unit 22104-φ of the computing apparatus 221-φ (FIG. 32) generates and outputs first input information τ_(φ,1) and second input information τ_(φ,2) each of which corresponds to an input element x_(φ) (step S22103 of FIG. 37). A process at step S22103 of this embodiment will be described below with reference to FIG. 39.

The first random number generator 22104 a-φ (FIG. 34) generates a uniform random number r(φ, 1) that is a natural number greater than or equal to 0 and less than K_(φ,H). The generated random number r(φ, 1) is sent to the first input information computing unit 22104 b-φ and the first computing unit 22105-φ (step S22103 a). The first input information computing unit 22104 b-φ uses the input random number r(φ, 1), the element x_(φ) and a natural number b(φ) to compute first input information τ_(φ,1)=μ_(φ,h) ^(r(φ,1))x_(φ) ^(b(φ)) (step S22103 b).

The second random number generator 22104 c-φ generates a uniform random number r(φ, 2) that is a natural number greater than or equal to 0 and less than K_(φ,H). The generated random number r(φ, 2) is sent to the second input information computing unit 22104 d-φ and the second computing unit 22108-φ (step S22103 c). The second input information computing unit 22104 d-φ uses the input random number r(φ, 2), the element x_(φ), and a natural number a(φ) to compute second input information τ_(φ,2)=μ_(φ,h) ^(r(φ,2))x_(φ) ^(a(φ)) (step S22103 d).

The first input information computing unit 22104 b-φ and the second input information computing unit 22104 d-φ output the first input information τ_(φ,1) and the second input information τ_(φ,2) thus generated (step S22103 e). Note that the first input information τ_(φ,1) and the second input information τ_(φ,2) of this embodiment are information whose relation with the element x_(φ) is scrambled using random numbers r(φ, 1) and r(φ, 2), respectively. This enables the computing apparatus 221-φ to conceal the element x_(φ) from the capability providing apparatus 222. The first input information τ_(φ,1) of this embodiment further corresponds to the natural number b(φ) selected by the natural number selecting unit 21102-φ and the second input information τ_(φ,2) further corresponds to the natural number a(φ) selected by the natural number selecting unit 21102-φ. This enables the computing apparatus 221-φ to evaluate the computing capability provided by the capability providing apparatus 222 with a high degree of accuracy.

<<Processes at Steps S22200 Through S22203>>

As illustrated in FIG. 38, first, the first input information τ_(φ,1)=μ_(φ,h) ^(r(φ,1))x_(φ) ^(b(φ)) is input in the first output information computing unit 22201 of the capability providing apparatus 222 (FIG. 33) and the second input information τ_(φ,2)=μ_(φ,h) ^(r(φ,2))x_(φ) ^(a(φ)) is input in the second output information computing unit 22202 (step S22200).

The first output information computing unit 22201 uses the first input information τ_(φ,1)=μ_(φ,h) ^(r(φ,1))x_(φ) ^(b(φ)) to correctly compute f_(φ)(μ_(φ,h) ^(r(φ,1))x_(φ) ^(b(φ))) with a probability greater than a certain probability and sets the obtained result of the computation as first output information z_(φ,1). The result of the computation may or may not be correct. That is, the result of the computation by the first output information computing unit 22201 may or may not be f_(φ)(μ_(φ,h) ^(r(φ,1))x_(φ) ^(b(φ))) (step S22201).

The second output information computing unit 22202 uses the second input information τ_(φ,2)=μ_(φ,h) ^(r(φ,2))x_(φ) ^(a(φ)) to correctly compute f_(φ)(μ_(φ,h) ^(r(φ,2))x_(φ) ^(a(φ))) with a probability greater than a certain probability and sets the obtained result of the computation as second output information z_(φ,2). The result of the computation may or may not be correct. That is, the result of computation by the second output information computing unit 22202 may or may not be f_(φ)(μ_(φ,h) ^(r(φ,2))x_(φ) ^(a(φ))) (step S22202).

The first output information computing unit 22201 outputs the first output information z_(φ,1) and the second output information computing unit 22202 outputs the second output information z_(φ,2) (step S22203).

<<Processes at Steps S22104 and S22105>>

Returning to FIG. 37, the first output information z_(φ,1) is input in the first computing unit 22105-φ of the computing apparatus 221-φ (FIG. 32) and the second input information z_(φ,2) is input in the second computing unit 22108-φ. The first output information z_(φ,1) and the second output information z_(φ,2) are equivalent to the computing capability provided by the capability providing apparatus 222 to the computing apparatus 221-φ (step S22104).

The first computing unit 22105-φ uses the input random number r(φ, 1) and the first output information z_(φ,1) to compute z_(φ,1)ν_(φ) ^(−r(φ,1)) and sets the result of the computation as u_(φ). The result u_(φ) of the computation is sent to the first power computing unit 21106-φ. Here, u_(φ)=z_(φ,1)ν_(φ) ^(−r(φ,1))=f_(φ)(x_(φ))^(b(φ))x_(φ,1). That is, z_(φ,1)ν_(φ) ^(−r(φ,1)) is an output of a randomizable sampler having an error X_(φ,1) for f_(φ)(x_(φ)). The reason will be described later (step S22105).

<<Process at Step S22108>>

The second computing unit 22108-φ uses the input random number r(φ, 2) and the second output information z_(φ,2) to compute z_(φ,2)ν_(φ) ^(−r(φ,2)) and sets the result of the computation as v_(φ). The result v_(φ) of the computation is sent to the second power computing unit 21109-φ. Here, v_(φ)=z_(φ,2)ν_(φ) ^(−r(φ,2))=f_(φ)(x_(φ))^(a(φ))x_(φ,2). That is, z_(φ,2)ν_(φ) ^(−r(φ,2)) is an output of a randomizable sampler having an error X_(φ,2) for f_(φ)(x_(φ)). The reason will be described later (step S22108).

<<Reason why z_(φ,1)ν_(φ) ^(−r(φ,1)) and z_(φ,2)ν_(φ) ^(−r(φ,2)) are Outputs of Randomizable Samplers Having Errors X_(φ,1) and X_(φ,2), Respectively, for f_(φ)(x_(φ))>>

Let c be a natural number, R and R′ be random numbers, and B(μ_(φ,h) ^(R)x_(φ) ^(c)) be the result of computation performed by the capability providing apparatus 222 using μ_(φ,h) ^(R)x_(φ) ^(c). That is, the results of computations that the first output information computing unit 22201 and the second output information computing unit 22202 return to the computing apparatus 221-φ are z_(φ)=B(μ_(φ,h) ^(R)x_(φ) ^(c)). A random variable X_(φ) that has a value in the group G_(φ) is defined as X_(φ)=B(μ_(φ,h) ^(R′))f_(φ)(μ_(φ,h) ^(R′))⁻¹.

Then, z_(φ)ν_(φ) ^(−R)=B(μ_(φ,h) ^(R)x_(φ) ^(c))f(μ_(φ,h))^(−R)=X_(φ)f_(φ)(μ_(φ,h) ^(R)x_(φ) ^(c))f_(φ)(μ_(φ,h))^(−R)=X_(φ)f_(φ)(μ_(φ,h))^(R)f_(φ)(x_(φ))^(c)f_(φ)(μ_(φ,h))^(−R)=f_(φ)(x_(φ))^(c)X_(φ). That is, z_(φ)ν_(φ) ^(−R) is an output of a randomizable sampler having an error X_(φ) for f_(φ)(x_(φ)).

The expansion of formula given above uses the properties such that X_(φ)=B(μ_(φ,h) ^(R′))f_(φ)(μ_(φ,h) ^(R′))⁻¹=B(μ_(φ,h) ^(R)x_(φ) ^(c))f_(φ)(μ_(φ,h) ^(R)x_(φ) ^(c))⁻¹) and that B(μ_(φ,h) ^(R)x_(φ)c)=X_(φ)f_(φ)(μ_(φ,h) ^(R)x_(φ) ^(c)). The properties are based on the fact that the function f_(φ) is a homomorphic function and R and R′ are random numbers.

Therefore, considering that a(φ) and b(φ) are natural numbers and r(φ, 1) and r(φ, 2) are random numbers, z_(φ,1)ν^(−r(φ,1)) and z_(φ,2)ν^(−r(φ,2)) are, likewise, outputs of randomizable samplers having errors X_(φ,1) and X_(φ,2), respectively, for f_(φ)(x_(φ)).

Fourteenth Embodiment

A fourteenth embodiment is a variation of the thirteenth embodiment and computes a value of u_(φ) or v_(φ) by using samplers described above when a(φ)=1 or b(φ)=1. The amounts of computations performed by samplers in general are smaller than the amounts of computations by randomizable samplers. Using samplers instead of randomizable samplers for computations when a(φ)=1 or b(φ)=1 can reduce the amounts of computations by the proxy computing system. The following description will focus on differences from the twelfth and thirteenth embodiments and repeated description of commonalities with the twelfth and thirteenth embodiments will be omitted.

<Configuration>

As illustrated in FIG. 31, a proxy computing system 203 of the fourteenth embodiment includes a computing apparatus 231-φ in place of the computing apparatus 221-φ and a capability providing apparatus 232 in place of the capability providing apparatus 222.

As illustrated in FIG. 32, the computing apparatus 231-φ of the fourteenth embodiment includes, for example, a natural number storage 21101-φ, a natural number selecting unit 21102-φ, an integer computing unit 21103-φ, an input information providing unit 22104-φ, a first computing unit 22105-φ, a first power computing unit 21106-φ, a first list storage 21107-φ, a second computing unit 22108-φ, a second power computing unit 21109-φ, a second list storage 21110-φ, a determining unit 21111-φ, a final output unit 21112-φ, a controller 21113-φ, and a third computing unit 23109-φ.

As illustrated in FIG. 33, the capability providing apparatus 232 of the fourteenth embodiment includes, for example, a first output information computing unit 22201, a second output information computing unit 22202, a controller 21205, and a third output information computing unit 23203.

<Processes>

Processes of this embodiment will be described below. What follows is a description of differences from the thirteenth embodiment.

As illustrated in FIGS. 37 and 38, a process of the fourteenth embodiment is the same as the process of the thirteenth embodiment except that steps S22103 through S22105, S22108, and S22200 through S22203 of the thirteenth embodiment are replaced with steps S23103 through S23105, S23108, S22200 through S22203, and S23205 through 23209, respectively. The following description will focus on processes at steps S23103 through S23105, S23108, S22200 through S22203, and S23205 through S23209.

<<Process at Step S23103>>

The input information providing unit 23104-φ of the computing apparatus 231-φ (FIG. 32) generates and outputs first input information τ_(φ,1) and second input information τ_(φ,2) each of which corresponds to an input element x_(φ) (step S23103 of FIG. 37).

A process at S23103 of this embodiment will be described below with reference to FIG. 39.

The controller 21113-φ (FIG. 32) controls the input information providing unit 23104-φ according to natural numbers (a(φ), b(φ)) selected by the natural number selecting unit 21102-φ.

Determination is made by the controller 21113-φ as to whether b(φ) is equal to 1 (step S23103 a). If it is determined that b(φ)≠1, the processes at steps S22103 a and 22103 b described above are performed and the process proceeds to step S23103 g.

On the other hand, if it is determined at step S23103 a that b(φ)=1, the third random number generator 23104 e-φ generates a random number r(φ, 3) that is a natural number greater than or equal to 0 and less than K_(φ,H). The generated random number r(φ,3) is sent to the third input information computing unit 23104 f-φ and the third computing unit 23109-φ (step S23103 b). The third input information computing unit 23104 f-φ uses the input random number r(φ, 3) and the element x_(φ) to compute x_(φ) ^(r(φ,3)) and sets it as first input information τ_(φ,1) (step S23103 c). Then the process proceeds to step S23103 g.

At step S23103 g, determination is made by the controller 21113 a-φ as to whether a(φ) is equal to 1 (step S23103 g). If it is determined that a(φ)≠1, the processes at steps S22103 c and S22103 d described above are performed.

On the other hand, if it is determined at step S23103 g that a(φ)=1, the third random number generator 23104 e-φ generates a random number r(φ, 3) that is a natural number greater than or equal to 0 and less than K_(φ,H). The generated random number r(φ, 3) is sent to the third input information computing unit 23104 f-φ (step S23103 h). The third input information computing unit 23104 f-φ uses the input random number r(φ, 3) and the element x_(φ) to compute x_(φ) ^(r(φ,3)) and sets it as second input information τ_(φ,2) (step S23103 i).

The first input information computing unit 22104 b-φ, the second input information computing unit 22104 d-φ, and the third input information computing unit 23104 f-φ output the first input information τ_(φ,1) and the second input information τ_(φ,2) thus generated along with information on the corresponding natural numbers (a(φ), b(φ)) (step S23103 e). Note that the first input information τ_(φ,1) and the second input information τ_(φ,2) in this embodiment are information whose relation with the element x_(φ) is scrambled using the random numbers r(φ, 1), r(φ, 2) and r(3, φ). This enables the computing apparatus 231-φ to conceal the element x_(φ) from the capability providing apparatus 232.

<<Processes at S22200 Through S22203 and S23205 Through S23209>>

Processes at S22200 through S22203 and S23205 through S23209 of this embodiment will be described below with reference to FIG. 38.

The controller 21205 (FIG. 33) controls the first output information computing unit 22201, the second output information computing unit 22202 and the third output information computing unit 23203 according to input natural numbers (a(φ), b(φ)).

Under the control of the controller 21205, the first input information τ_(φ,1)=μ_(φ,h) ^(r(φ,1))x_(φ) ^(b(φ)) when b(φ)≠1 is input in the first output information computing unit 22201 of the capability providing apparatus 232 (FIG. 33) and the second input information τ_(φ,2)=μ_(φ,h) ^(r(φ,2))x_(φ) ^(a(φ)) when a(φ)≠1 is input in the second output information computing unit 22202. The first input information τ_(φ,1)=x_(φ) ^(r(φ,3)) when b(φ)=1 and the second input information τ_(φ,2)=x_(φ) ^(r(φ,3)) when a(φ)=1 are input in the third output information computing unit 23203 (step S23200).

Determination is made by the controller 21113-φ as to whether b(φ) is equal to 1 (step S23205). If it is determined that b(φ)≠1, the process at step S22201 described above is performed. Then, determination is made by the controller 21113-φ as to whether a(φ) is equal to 1 (step S23208). If it is determined that a(φ)≠1, the process at step S22202 described above is performed and then the process proceeds to step S23203.

On the other hand, if it is determined at step S23208 that a(φ)=1, the third output information computing unit 23203 uses the second input information τ_(φ,2)=x_(φ) ^(r(φ,3)) to correctly compute f_(φ)(x_(φ) ^(r(φ,3))) with a probability greater than a certain probability and sets the obtained result of the computation as third output information z_(φ,3). The result of the computation may or may not be correct. That is, the result of the computation by the third output information computing unit 23203 may or may not be f_(φ)(x(_(φ) ^(r(φ,3))) (step S23209). Then the process proceeds to step S23203.

If it is determined at step S23205 that b(φ)=1, the third output information computing unit 23203 uses the second input information τ_(φ,1)=x_(φ) ^(r(φ,3)) to correctly compute f_(φ)(x_(φ) ^(r(φ,3))) with a probability greater than a certain probability and sets the obtained result of the computation as third output information z_(φ,3). The result of the computation may or may not be correct. That is, the result of the computation by the third output information computing unit 23203 may or may not be f_(φ)(x_(φ) ^(r(φ,3))) (step S23206).

Then, determination is made by the controller 21113-φ as to whether a(φ) is equal to 1 (step S23207). If it is determined that a(φ)=1, the process proceeds to step S23203; if it is determined a(φ)≠1, the process proceeds to step S22202.

At step S23203, the first output information computing unit 22201, which has generated the first output information z_(φ,1), outputs the first output information z_(φ,1), the second output information computing unit 22202, which has generated the second output information z_(φ,2), outputs the second output information z_(φ,2), and the third output information computing unit 23202, which has generated the third output information z_(φ,3), outputs the third output information z_(φ,3) (step S23203).

<<Processes at Steps S23104 and S23105>>

Returning to FIG. 37, under the control of the controller 21113-φ, the first output information z_(φ,1) is input in the first computing unit 22105-φ of the computing apparatus 231-φ (FIG. 32), the second output information z_(φ,2) is input in the second computing unit 22108-φ, and the third output information z_(φ,3) is input in the third computing unit 23109-φ (step S23104).

If b(φ)≠1, the first computing unit 22105-φ performs the process at step S22105 described above to generate u_(φ); if b(φ)=1, the third computing unit 23109-φ computes Z_(φ,3) ^(1/r(φ,3)) and sets the result of the computation as u_(φ). The computation result u_(φ) is sent to the first power computing unit 21106-φ. Here, if b(φ)=1, then u_(φ)=z_(φ3) ^(1/r(φ,3))=f_(φ)(x_(φ))x_(φ,3). That is, z_(φ,3) ^(1/r(φ,3)) serves as a sampler having an error X_(φ,3) for f_(φ)(x_(φ)). The reason will be described later (step S23105).

<<Process at Step S23108>>

If a(φ)≠1, the second computing unit 22108-φ performs the process at step S22108 described above to generate v_(φ); if a(φ)=1, the third computing unit 23109-φ computes z_(φ,3) ^(1/r(φ,3)) and sets the result of the computation as v_(φ). The computation result v_(φ) is sent to the second power computing unit 21109-φ. Here, if a(φ)=1, then v_(φ)=z_(φ,3) ^(1/r(φ,3))=f_(φ)(x_(φ))x_(φ,3). That is, z_(φ,3) ^(1/r(φ,3)) serves as a sampler having an error X_(φ,3) for f_(φ)(x_(φ)). The reason will be described later (step S23108).

Note that if z_(φ,3) ^(1/r(φ,3)), that is, the radical root of z_(φ,3), is hard to compute, u_(φ) and/or v_(φ) may be calculated as follows. The third computing unit 23109-φ may store each pair of a random number r(φ, 3) and z_(φ,3) computed on the basis of that random number r(φ, 3) in a storage, not depicted, in sequence as (α₁, β₁), (α₂, β₂), . . . , (α_(m), β_(m)), . . . , where m is a natural number. The third computing unit 23109-φ may compute γ₁, γ₂, . . . , γ_(m) that satisfy γ₁α₁+γ₂α₂+ . . . +γ_(m)α_(m)=1 when the least common multiple of α₁, α₂, . . . , α_(m) is 1, where γ₁, γ₂, . . . , γ_(m) are integers. The third computing unit 32109-φ may then use the resulting γ₁, γ₂, . . . , γ_(m) to compute Π_(i=1) ^(m)β_(i) ^(γi)=β₁ ^(γ1)β₂ ^(γ2) . . . β_(m) ^(γm) and may set the results of the computation as u_(φ) and/or v_(φ).

<<Reason why z_(φ,3) ^(1/r(φ,3)) Serves as a Sampler Having an Error X_(φ,3) for f_(φ)(x_(φ))>>

Let R be a random number and B(x_(φ) ^(R)) be the result of computation performed by the capability providing apparatus 232 using x_(φ) ^(R). That is, let z_(φ)=B(x_(φ) ^(R)) be computation results returned by the first output information computing unit 22201, the second output information computing unit 22202, and the third output information computing unit 23203 to the computing apparatus 231-φ. Furthermore, a random variable X_(φ) having a value in the group G_(φ) is defined as X_(φ)=B(x_(φ) ^(R))^(1/R)f_(φ)(x_(φ))⁻¹.

Then, z_(φ) ^(1/R)=B(x_(φ) ^(R))^(1/R)=X_(φ)f_(φ)(x_(φ))=f_(φ)(x_(φ))X_(φ). That is, z_(φ) ^(1/R) serves as a sampler having an error X_(φ) for f_(φ)(x_(φ)).

The expansion of formula given above uses the properties such that X_(φ)=B(x_(φ) ^(R))^(1/R)f_(φ)(x_(φ) ^(R))⁻¹ and that B(x_(φ) ^(R))^(1/R)=X_(φ)f_(φ)(x_(φ) ^(R)). The properties are based on the fact that R is a random number.

Therefore, considering that r(φ, 3) is a random number, Z_(φ) ^(1/R) serves as a sampler having an error X_(φ,3) for f_(φ)(x_(φ)), likewise.

Fifteenth Embodiment

A proxy computing system of a fifteenth embodiment is another example that embodies the first and second randomizable samplers described above. Specifically, in this embodiment, f_(φ)(x_(φ)) is a function for converting an element x_(φ)=C_(φ,1)(y(φ,1), m_(φ)) of a group H_(φ) which is a first ciphertext to a second ciphertext f_(φ)(x_(φ))=C_(φ,2)(y(φ,2), m_(φ)) which is an element of a group G_(φ). Here, the first ciphertext C_(φ,1)(y(φ, 1), m_(φ)) is a ciphertext obtained by encrypting a plaintext m_(φ) with a first encryption key y(φ, 1) according to a first encryption scheme ENC_(φ,1) and the second ciphertext C_(φ,2)(y(φ, 2), m_(φ)) is a ciphertext obtained by encrypting the plaintext m_(φ) with a second encryption key y(φ, 2) according to a second encryption scheme ENC_(φ,2). The second encryption scheme ENC_(φ,2) is the ElGamal encryption and the function f_(φ)(x_(φ)) is a homomorphic function. The first encryption scheme ENC_(φ,1) may be any encryption scheme; the first encryption scheme ENC_(φ,1) may be probabilistic encryption such as the ElGamal encryption or may be deterministic encryption such as RSA encryption.

The following description will focus on differences from the twelfth embodiment and repeated description of commonalities with the twelfth embodiment will be omitted.

As illustrated in FIG. 31, a proxy computing system 204 of the fifteenth embodiment includes a computing apparatus 241-φ in place of the computing apparatus 221-φ and a capability providing apparatus 242 in place of the capability providing apparatus 212.

As illustrated in FIG. 32, the computing apparatus 241-φ of the fifteenth embodiment includes, for example, a natural number storage 21101-φ, a natural number selecting unit 21102-φ, an integer computing unit 21103-φ, an input information providing unit 24104-φ, a first computing unit 24105-φ, a first power computing unit 21106-φ, a first list storage 21107-φ, a second computing unit 24108-φ, a second power computing unit 21109-φ, a second list storage 21110-φ, a determining unit 24111-φ, a final output unit 21112-φ, and a controller 21113-φ. As illustrated in FIG. 35, the input information providing unit 24104-φ of this embodiment includes, for example, a first random number generator 24104 a-φ, a first input information computing unit 24104 b-φ, a second random number generator 24104 c-φ, and a second input information computing unit 24104 d-φ.

As illustrated in FIG. 33, the capability providing apparatus 242 of the fifteenth embodiment includes, for example, a first output information computing unit 24201, a second output information computing unit 24202, and a controller 21205.

<Assumptions for Processes>

In the fifteenth embodiment, the group G_(φ) is the direct product group G_(φ,1)×G_(φ,2) of cyclic groups G_(φ,1) and G_(φ,2), μ_(φ,g1) is a generator of the group G_(φ,1), μ_(φ,g2) is a generator of the group G_(φ,2), the second encryption key y(φ, 2) is μ_(φ,g2) ^(s(φ,2)), an element C_(φ,2)(y(φ, 2), m_(φ)) is (μ_(φ,g1) ^(r(φ)), m_(φ)y(φ, 2)^(r(φ)))ϵG_(φ,1)×G_(φ,2), r(φ) is an integer random number, a value u_(φ) ^(a(φ)) is (c_(φ,1u), c_(φ,2u))ϵG_(φ,1)×G_(φ,2), and a value v_(φ) ^(b(φ)) is (c_(φ,1v), C_(φ,2v))ϵG_(φ,1)×G_(φ,2). G_(φ,1) may be equal to G_(φ,2) or may be unequal to G_(φ,2). The first encryption scheme ENC_(φ,1) may be any encryption scheme as stated above. If the first encryption scheme ENC_(φ,1) is the ElGamal encryption, the group H_(φ) is the direct product group H_(φ,1)×H_(φ,2) of cyclic groups H_(φ,1) and H_(φ,2), r′(φ) is an integer random number, μ_(φ,h1) is a generator of the group H_(φ,1), μ_(φ,h2) is a generator of the group H_(φ,2), the first encryption key y(φ, 1) is μ_(φ,h2) ^(s(φ,1)), the first ciphertext C_(φ,1)(y(φ, 1), m_(φ)) is (μ_(φ,h1) ^(r′(φ)), m_(φ)y(φ, 1)^(r′(φ)))ϵH_(φ,1)×H_(φ2). H_(φ,1) may be equal to H_(φ,2) or may be unequal to H_(φ,2).

Note that if A=(α₁, α₂)ϵG_(φ,1)×G_(φ,2), B=(β₁, β₂)ϵG_(φ,1)×G_(φ,2), and ϵ is a natural number, then A^(ϵ) represents (α₁ ^(ϵ), α₂ ^(ϵ)), A^(−ϵ) represents (α₁ ^(−ϵ), α₂ ^(−ϵ)), and AB represents (α₁β₁, α₂β₂). Similarly, if ϵ is a natural number, A=(α₁, α₂)ϵH_(φ,1)×H_(φ2), and B=(β₁,β₂)ϵH_(φ,1)×H_(φ,2), then A^(ϵ) represents (α₁ ^(ϵ), α₂ ^(ϵ)), A^(−ϵ) represents (α₁ ^(−ϵ), α₂ ^(−ϵ)), and AB represents (α₁β₁, α₂β₂). e_(φ)(α, β) is a bilinear map that gives an element of a cyclic group G_(φ,T) for (α, β)ϵG_(φ,1)×G_(φ,2). Examples of the bilinear map include functions and algorithms for performing pairing computations such as Weil pairing and Tate pairing (see Reference literature 2: Alfred J. Menezes, “ELLIPTIC CURVE PUBLIC KEY CRYPTOSYSTEMS”, KLUWER ACADEMIC PUBLISHERS, ISBN 0-7923-9368-6, pp. 61-81 and Reference literature 3: RFC 5091, “Identity-Based Cryptography Standard (IBCS) #1”, Supersingular Curve Implementations of the BF and BB1 Cryptosystems, for example).

<Processes>

As illustrated in FIGS. 37 and 38, a process of the fifteenth embodiment is the same as the process of the twelfth embodiment except that steps S21103 through S21105, S21107, S21108, S21110, and S21200 through S21203 of the twelfth embodiment are replaced with steps S24103 through S24105, S241017, S24108, S24110, and S24200 through S24203, respectively. In the following, only processes at steps S24103 through S24105, S24107, S24108, S24110, and S24200 through S24203 will be described.

<<Process at Step S24103>>

The input information providing unit 24104-φ of the computing apparatus 241-φ (FIG. 32) generates and outputs first input information τ_(φ,1) and second input information τ_(φ,2) corresponding to an input element x_(φ)=C_(φ,1)(y(φ, 1), m_(φ)) (step S24103 of FIG. 37). The process at step S24103 of this embodiment will be described below with reference to FIG. 40.

The first random number generator 24104 a-φ (FIG. 35) generates an arbitrary element _(h)r_(100,1)ϵH_(φ) of the group H_(φ). In this embodiment an element _(h)r_(φ,1) is randomly and uniformly selected from the group H_(φ) (uniform random number). The generated element _(h)r_(φ,1) is sent to the first input information computing unit 24104 b-φ and the first computing unit 24105-φ (step S24103 a).

The first input information computing unit 24104 b-φ uses a natural number b(φ) selected by the natural number selecting unit 21102-φ, the element x_(φ), the element _(h)r_(φ,1), and the first encryption key y(φ, 1) to compute x_(φ) ^(b(φ))C_(φ,1)(y(φ, 1), _(h)r_(φ,1)) as first input information τ_(φ,1) (step S24103 b).

The second random number generator 24104 c-φ generates an arbitrary element _(h)r_(φ,2)ϵH_(φ) of the group H_(φ). In this embodiment an element _(h)r_(φ,2) is randomly and uniformly selected from the group H_(φ) (uniform random number). The generated element _(h)r_(φ,2) is sent to the second input information computing unit 24104 d-φ and the second computing unit 24108-φ (step S24103 c).

The second input information computing unit 24104 b-φ uses a natural number a(φ) selected by the natural number selecting unit 21102-φ, the element x_(φ), the element _(h)r_(φ,2), and the first encryption key y(φ, 1) to compute x_(φ) ^(a(φ))C_(φ,1)(y(φ, 1), _(n)r_(φ,2)) as second input information τ_(φ,2) (step S24103 d).

The first input information computing unit 24104 b-φ outputs the first input information τ_(φ,1)=x_(φ) ^(b(φ))C_(φ,1)(y(φ, 1), _(h)r_(φ,1)) computed as described above. The second input information computing unit 24104 d-φ outputs the second input information τ_(φ,2)=x_(φ) ^(a(φ))C_(φ,1)(y(φ, 1), _(h)r_(φ,2)) computed as described above (step S24103 e).

<<Processes at Steps S24200 Through S24203>>

As illustrated in FIG. 38, first, the first input information τ_(φ,1)=x_(φ) ^(b(φ))C_(φ,1)(y(φ, 1), _(h)r_(φ,1)) is input in the first output information computing unit 24201 of the capability providing apparatus 242 (FIG. 33) and the second input information τ_(φ,2)=x_(φ) ^(a(φ))C_(φ,1)(y(φ, 1), _(h)r_(φ,2)) is input in the second output information computing unit 24202 (step S24200).

The first output information computing unit 24201 uses the first input information τ_(φ,1)=x_(φ) ^(b(φ))C_(φ,1) (y(φ, 1), _(h)r_(φ,1)), a first decryption key s(φ, 1) corresponding to the first encryption key y(φ, 1), and the second encryption key y(φ, 2) to correctly compute f_(φ)(x_(φ) ^(b(φ))C_(φ,1)(y(φ, 1), _(h)r_(φ,1))) with a probability greater than a certain probability and sets the result of the computation as first output information z_(φ,1). The result of the computation may or may not be correct. That is, the result of the computation by the first output information computing unit 24201 may or may not be f_(φ)x(_(φ) ^(b(φ))C_(φ,1)(y(φ, 1), _(h)r_(φ,1))) (step S24201).

Note that the function f_(φ) in this embodiment is a homomorphic function for encrypting a value that can be obtained by decrypting a ciphertext with the first decryption key s(φ, 1) according to the first encryption scheme ENC_(φ,1) with the second encryption key y(φ, 2) according to the ElGamal encryption. For example, if both of the first encryption scheme ENC_(φ,1) and the second encryption scheme ENC_(φ,2) are the ElGamal encryption, the function f_(φ) is a homomorphic function for encrypting a value that can be obtained by decrypting a ciphertext with the first decryption key s(φ, 1) according to the ElGamal encryption with the second encryption key y(φ, 2) according to the ElGamal encryption.

The second output information computing unit 24202 can use the second input information τ_(φ,2)=x_(φ) ^(a(φ))c_(φ, 1) (y(φ, 1), _(h)r_(φ,2)), the first decryption key s(φ, 1), and the second encryption key y(φ, 2) to correctly compute f_(φ)(x_(φ) ^(a(φ))C_(φ,1)(y(φ, 1), _(h)r_(φ,2))) with a probability greater than a certain probability and sets the result of the computation as second output information z_(φ,2). The result of the computation may or may not be correct. That is, the result of the computation by the second output information computing unit 24202 may or may not be f_(φ)(x_(φ) ^(a(φ))C_(φ,1)(y(φ, 1), _(h)r_(φ,2)))(step S24202).

The first output information computing unit 24201 outputs the first output information z_(φ,1) and the second output information computing unit 24202 outputs the second output information z_(φ,2) (step S24203).

<<Processes at Steps S24104 and S24105>>

Returning to FIG. 37, the first output information z_(φ,1) is input in the first computing unit 24105-φ of the computing apparatus 241-φ (FIG. 32) and the second output information z_(φ,2) is input in the second computing unit 24108-φ (step S24104).

The first computing unit 24105-φ uses the input first output information z_(φ,1), the element _(h)r_(φ,1), and the second encryption key y(φ, 2) to compute z_(φ,1)(C_(φ,2)(y(φ, 2), _(h)r_(φ,1)))⁻¹ and sets the result of the computation as u_(φ) (step S24105). The result u_(φ) of the computation is sent to the first power computing unit 21106-φ. Here, u_(φ)=z_(φ,1)(C_(φ,2)(y(φ, 2), _(h)r_(φ,1)))⁻¹=f_(φ)(x_(φ))^(b(φ))x_(φ,1). That is, z_(φ,1)(C_(φ,2)(y(φ, 2), _(h)r_(φ,1)))⁻¹ is an output of a randomizable sampler having an error X_(φ,1) for f_(φ)(x_(φ)). The reason will be described later.

<<Process at Step S24108>>

The second computing unit 24108-φ uses the input second output information z_(φ,2), the element _(h)r_(φ,2), and the second encryption key y(φ, 2) to compute z_(φ,2)(C_(φ,2)(y(φ, 2), _(h)r_(φ,2)))⁻¹ and sets the result of the computation as v_(φ). The result v_(φ) of the computation is sent to the second power computing unit 21109-φ. Here, v_(φ)=z_(φ,2)(C_(φ,2)(y(φ, 2), _(h)r_(φ,2)))⁻¹=f_(φ)(x_(φ))^(a(φ))x_(φ,2). That is, z_(φ,2)(C_(φ,2)(y(φ, 2), _(h)r_(φ,2)))⁻¹ is an output of a randomizable sampler having an error X_(φ,2) for f_(φ)(x_(φ)). The reason will be described later.

<<Process at Step S24107>>

The determining unit 24111-φ determines whether or not there is a pair of u_(φ)′ and v_(φ)′ that belong to a class CL_(φ)(M_(φ)) corresponding to the same element M_(φ) among the pairs (u_(φ), u_(φ)′) stored in the first list storage 21107-φ and the pairs (v_(φ), v_(φ)′) stored in the second list storage 21110-φ. The determining unit 24111-φ of this embodiment determines whether or not there is a pair that satisfies the relation e_(φ)(μ_(φ,g1), c_(φ,2u))/e_(φ)(c_(φ,1u), y(φ,2))=e_(φ)(μ_(φ,g1), c(_(φ,2v))/e_(φ)(c_(φ,1v), y(φ, 2)) for u_(φ)′=(c_(φ, 1u), c_(φ,2u)) and v_(φ)′=(c_(φ,1v), c_(φ,2v)) (step S24107). The reason why the determination as to whether u_(φ)′ and v_(φ)′ belong to the class CL_(φ)(M_(φ)) corresponding to the same element M_(φ) can be made by determining whether u_(φ)′ and v_(φ)′ satisfy the relation will be described later.

If a pair (v_(φ), v_(φ)′) is not stored in the second list storage 21110-φ, a process at step S21108 is performed without performing the process at step S24107. If there is a pair of u_(φ)′ and v_(φ)′ that belong to the class CL_(φ)(M_(φ)) corresponding to the same element M_(φ)(if there is a pair of u_(φ)′ and v_(φ)′ that satisfy the relation given above), the process proceeds to step S21114. If there is not a pair of u_(φ)′ and v_(φ)′ that belong to the class CL_(φ)(M_(φ)) corresponding to the same element M_(φ), the process proceeds to step S24108.

<<Process at Step S24110>>

The determining unit 24111-φ determines whether there is a pair of u_(φ)′ and v_(φ)′ that belong to a class CL_(φ)(M_(φ)) corresponding to the same element M_(φ) among the pairs (u_(φ), u_(φ)′) stored in the first list storage 21107-φ and the pairs (v_(φ), v_(φ)′) stored in the second list storage 24110-φ. The determining unit 24111-φ of this embodiment determines whether there is a pair that satisfies the relation e_(φ)(μ_(φ,g1), c_(φ,2u))/e_(φ)(c_(φ,1u), y(φ, 2))=e_(φ)(μ_(φ,g1), c_(φ,2v))/e_(φ)(c_(φ,1v), y(φ, 2)) for u_(φ)′=c_(φ,1u), _(φ,2u)) and v_(φ)′=(c_(φ,1v), c_(φ,2v)) (step S24110). If there is a pair of u_(φ)′ and v_(φ)′ that belong the class CL_(φ)(M_(φ)) corresponding to the same element M_(φ), the process proceeds to step S21114. If there is not a pair of u_(φ)′ and v_(φ)′ that belong to the class CL_(φ)(M_(φ)) corresponding to the same element M_(φ), the process proceeds to step S21111.

Note that using special groups G_(φ,1), G_(φ,2) and the bilinear map e_(φ) can allow only a permitted person to cause the computing apparatus 241-φ to perform the processes of steps S24107 and S24110. Details of this will be described later.

<<Reason why z_(φ,1)(C_(φ,2)(y(φ, 2), _(h)r_(φ,1)))⁻¹ and z_(φ,2)(C_(φ,2)(y(φ, 2), _(h)r_(φ2)))⁻¹ are Outputs of Randomizable Samplers Having Errors X_(φ,1) and X_(φ,2), Respectively, for f_(φ)(x_(φ))>>

Suppose that an arbitrary element _(h)r_(φ,1) is fixed, then the relation given below holds for a probability distribution over a probability space of random numbers r(φ) in elements C_(φ,2)(y(φ, 2), m_(φ))=(μ_(φ,g1) ^(r(φ)), m_(φ),y(φ, 2)^(r(φ)): [z _(φ,2)(C _(φ,2)(y((φ,2),_(h) r(_(φ,2)))⁻¹] =[f _(φ)(x _(φ) ^(a(φ)) C _(φ,2)(y(φ,1),_(h) r _(φ,2)))(C _(φ,2)(y(φ,2),_(h) r _(φ,2)))] =[f _(φ)(C _(φ,2)(y(φ,1),D _(φ,1)(s(φ,1),x _(φ))^(a(φ)) _(h) r _(φ,2)))(C _(φ,2)(y((φ,2),_(h) r _(φ,2)))⁻¹] =[f _(φ)(C _(φ,2)(y(φ,1),D _(φ,1)(s(φ,1),x _(φ))^(a(φ)) _(h) r _(p2)))C _(φ,2)(y(φ,2),D _(φ,1)(s(φ,1),x(_(φ))^(a(φ)) _(h) r _(φ,2))⁻¹ C _(φ,2)(y(φ,2),D _(φ,1)(s(ι),1),x(_(φ))^(a(φ)))] =[f _(φ)(C _(φ,2)(y(φ,1),_(h) r _(φ,2)′))C _(φ,2)(y(φ,2),_(h) r _(φ,2)′)⁻¹ C _(φ,2)(y(φ,2),D _(φ,1)(s(φ,1),x(_(φ))^(a)(φ))] where [ψ₁]=[ψ₂] means that ψ₁ is equal to ψ₂ as probability distributions over probability spaces of random numbers r. D_(φ,1)(s(φ, 1), x_(φ)) represents a function for decrypting elements x_(φ) with the first decryption key s(φ, 1) according to a first encryption scheme ENC_(φ,1). It is assumed that _(h)r_(φ,2)′=D_(φ,1)(s(φ, 1), x_(φ))^(a(φ)) _(h)r_(φ,2).

Therefore, assuming that both of the random number r(φ) and the uniform random element _(g)r_(φ) on the group G_(φ) are probability spaces and a random variable having a value in the group G_(φ) is X_(φ,2)=f_(φ)(C_(φ,2)(y(φ, 1), _(h)r_(φ,2)′))C(_(φ,2)(y(φ, 2), _(h)r_(φ,2)′)⁻¹, then the relation given below holds for x_(φ,2)ϵX_(φ,2): z _(φ,2)(C _(φ,2)(y(φ,2),_(h) r _(φ,2)))⁻¹ =x _(φ,2) C _(φ,2)(y(φ,2),D _(φ,1)(s(φ,1),x(_(φ))^(a(φ))) =X _(φ,2) f _(φ)(x _(φ) ^(a(φ))) =f _(φ)(λ(_(φ))^(a(φ)) x _(φ2)

Likewise, the relation z_(φ,1)(C_(φ,2)(y(φ, 2), _(h)r_(φ,1)))⁻¹=f_(φ)(x_(φ))^(b(φ))x_(φ,1) holds for x_(φ,1)ϵX_(φ,1). Therefore, z_(φ,1)(C_(φ,2)(y(φ, 2), _(n)r_(φ,1)))⁻¹ and z_(φ,2)(C_(φ,2)(y(φ, 2), _(h)r_(φ,2)))⁻¹ are outputs of randomizable samples having errors X_(φ,1) and X_(φ,2), respectively, for f_(φ)(x_(φ)).

<<Why Determining Whether Relation e_(φ)(μ_(φ,g1), c_(φ2u))/e_(φ)(c_(φ,1u), y(φ, 2))=e_(φ)(μ_(φ,g1), c_(φ,2v))/e_(φ)(c_(φ,1v), y(φ, 2)) is Satisfied can Determine Whether u_(φ)′ and v_(φ)′ Belong to Class CL_(φ)(M_(φ)) Corresponding to the Same M_(φ)>>

Assume that u_(φ)′ and v_(φ)′ belong to a class CL_(φ)(M_(φ)) corresponding to the same element M_(w)=x_(φ). Then, it is highly probable that the first randomizable sampler has correctly computed u_(φ)=f(x_(φ))^(b(φ)) and that the second randomizable sampler has correctly computed v_(φ)=f_(φ)(x_(φ))^(a(φ)) (that is, x_(φ,1) and x_(φ,2) are identity elements e_(φ,g) of the group G_(φ)). Accordingly, it is highly probable that u_(φ)′=(μ_(φ,g1) ^(r″(φ)), m_(φ)y(φ, 2)^(r″(φ)) and v_(φ)′=(μ_(φ,g1) ^(r′″(φ)), m_(φ)y(φ, 2)^(r′″(φ))), where r″(φ) and r′″(φ) are values that are determined by a pair of a random number component of ElGamal encryption and a natural number selected by the natural number selecting unit 21102-φ. Then, from the properties of the bilinear map e_(φ), it is highly probable for u_(φ)′=(c_(φ,1u), c_(φ,2u)), the following is satisfied: e _(φ)(μ_(φ,g1) ,c _(φ,2u))/e _(φ)(c _(φ,1u) ,y(φ,2)) =e _(φ)(μ_(φ,g1) ,m _(φ) y(φ,2)^(r″(φ)) /e _(φ)(μ_(φ,g1) ^(r″(φ)) ,y(φ,2)) =e _(φ)(μ_(φ,g1) ,m _(φ) y(φ,2)^(r″(φ)) /e _(φ)(μ_(φ,g1) ,y(φ,2))^(r″(φ)) =e _(φ)(μ_(φ,g1) ,m _(φ))e _(φ)(μ_(φ,g1) ,y(φ,2))/e _(φ)(μ_(φ,g1) ,y(φ,2)) =e _(φ)(μ_(φ,g1) ,m _(φ)). For v_(φ)′=(c_(φ,1v), c_(φ,2v)), it is highly probable that the following is satisfied: e _(φ)(μ_(φ,g1) ,c _(φ,2v))/e _(φ)(c _(φ,1v) ,y(φ,2)) =e _(φ)(μ_(φ,g1) ,m _(φ) y(φ,2)^(r′″(φ)) /e _(φ)(μ_(φ,g1) ^(r′″(φ)) ,y(φ,2)) =e _(φ)(μ_(φ,g1) ,m _(φ) y(φ,2)^(r′″(φ)) /e _(φ)(μ_(φ,g1) ,y(φ,2))^(r′″(φ)) =e _(φ)(μ_(φ,g1) ,m _(φ))e _(φ)(μ_(φ,g1) ,y(φ,2))/e _(φ)(μ_(φ,g1) ,y(φ,2)) =e _(φ)(μ_(φ,g1) ,m _(φ)). Therefore, if u_(φ)′ and v_(φ)′ belong to the class CL_(φ)(M_(φ)) corresponding to the same element M_(φ), it is highly probable that the relation e_(φ)(μ_(φ,g1), c_(φ,2u))/e_(φ)(c_(φ,1u), y(φ, 2))=e_(φ)(μ_(φ,g1), C_(φ,2v))/e_(φ)(c_(φ,1v), y(φ, 2)) is satisfied.

Next, assume that u_(φ)′ and v_(φ)′ belong to classes corresponding to different elements. That is, u_(φ)′ belongs a class CL_(φ)(m_(φ,u)) corresponding to an element m_(φ,u) and v_(φ)′ belongs to a class CL_(φ)(m_(φ,v)) corresponding to an element m_(φ,v) (m_(φ,v)≠m_(φ,u)). Then, u_(φ)′=(μ_(φ,g1) ^(r″(φ)), m_(φ,u)y(φ, 2)^(r″(φ)))x_(φ,1) and v_(φ)′=(μ_(φ,g1) ^(r′″(φ)), m_(φ,v)y(φ, 2)^(r′″(φ)))x_(φ,2). Accordingly, e_(φ)(μ_(φ,g1), c_(φ,2u))/e_(φ)(c_(φ,1u), y(φ, 2))=e_(φ)(μ_(φ,g1), m_(φ,u))x_(φ,1) is satisfied for u_(φ)′=(c_(φ,1u), c_(φ,2u)) and e_(φ)(μ_(φ,g1), C_(φ,2v))/e_(φ)(c_(φ,1v), y(φ, 2))=e_(φ)(μ_(φ,g1), m_(φ,v))x_(φ,2) is satisfied for v_(φ)′=(c_(φ,1v), c_(φ,2v)). Therefore, if u_(φ)′ and v_(φ)′ belong to classes corresponding to different elements, it is highly probable that the relation e_(φ)(μ_(φ,g1), c_(φ,2))/e_(φ)(c_(φ,1u), y(φ, 2))≠e_(φ)(μ_(φ,g1), c_(φ,2v))/e_(φ)(c_(φ,1v), y(φ, 2)) is satisfied.

<<Special Groups G_(φ,1), G_(φ,2) and Bilinear Map e_(φ)>>

Setting the following restriction on the constructions of groups G_(φ,2) and the bilinear map e_(φ) can allow only a permitted person to cause the computing apparatus 241-φ to perform the processes of steps S24107 and S24110. Details of this will be described below.

In this special example, N_(φ) is the composite number of primes ω_(φ) and primes ι_(φ), groups G_(φ,1) and G_(φ,2) are subgroups consisting of points on a first elliptic curve E_(φ,1) defined on a factor ring Z/N_(φ)Z modulo composite number N_(φ), G_(φ,1ω) and G_(φ,2ω) are subgroups consisting of points on a second elliptic curve E_(φ,2) defined on a factor ring Z/ω_(φ)Z modulo prime ω_(φ), G_(φ,1ι) and G_(φ,2ι) are subgroups consisting of points on a third elliptic curve E_(φ,3) defined on a factor ring Z/ι_(φ)Z modulo prime ι_(φ), e_(φ)(α, β) is a bilinear map that yields an element of a cyclic group G_(φ,T) for (α, β)ϵG_(φ,1)×G_(φ,2), e_(φ,ω)(α_(ω), β_(ω)) is a second bilinear map that yields an element of a cyclic group G_(φ,Tω) for (α_(ω), β_(ω)) ϵG_(φ,1ω)×G_(φ,2ω), e_(φ,ι)(α_(ι), β_(ι)) is a third bilinear map that yields an element of a cyclic group G_(φ,Tι) for (α_(ι), β_(ι))ϵG_(φ,1ι)×G_(φ,2ι), HM_(φ) is an isomorphism map that maps a point on the first elliptic curve E_(φ,1) to a point on the second elliptic curve E_(φ,2), and a point on the third elliptic curve E_(φ,3), and HM_(φ) ⁻¹ is the inverse map of the isomorphism map HM_(φ).

In this example, the bilinear map e_(φ)(α, β) is defined on the first elliptic curve E_(φ,1) defined on the factor ring Z/N_(φ)Z. However, there is not a known method for computing the bilinear map e_(φ)(α, β) defined on an elliptic curve defined on a factor ring in polynomial time nor a method for constructing a bilinear map e_(φ)(α, β) defined on an elliptic curve defined on a factor ring that can be computed in polynomial time (Reference literature 4: Alexander W. Dent and Steven D. Galbraith, “Hidden Pairings and Trapdoor DDH Groups,” ANTS 2006, LNCS 4076, pp. 436-451, 2006). In such a setting, the determining unit 24111 cannot determine whether or not the relation e_(φ)(μ_(φ,g1), c_(φ,2u))/e_(φ)(c_(φ,1u), y(φ, 2))=e_(φ)(μ_(φ,g1), c_(φ,2v))/e_(φ)(c_(φ,1v), y(φ, 2)) is satisfied by directly computing the bilinear map e_(φ) on the factor ring Z/N_(φ)Z.

On the other hand, as for e_(φ,ω)(α_(ω), β_(ω)) and e_(φ,ι)(α_(ι), β_(ι)) defined on elliptic curves defined on residual fields Z/ω_(φ)Z and Z/ι_(φ)Z, there are pairings such as Weil pairing and Tate pairing which can be computed in polynomial time (see Reference literatures 2 and 3, for example). Algorithms for computing such e_(φ,ω)(α_(ω), β_(ω)) and e_(φ,1)(α_(ι), β_(ι)) in polynomial time, such as Miller's algorithm, are well known (Reference literature 5: V. S. Miller, “Short Programs for functions on Curves,” 1986, Internet <http://crypto.standford.edu/miller/miller.pdf>). Furthermore, methods for constructing elliptic curves and cyclic groups for efficiently computing such e_(φ,ω)(α_(ω), β_(ω)) and e_(φ,1)(α_(ι), β_(ι)) are also well known (See, for example, Reference literatures 3 and 6: A Miyaji, M. Nakabayashi, S. Takano, “New explicit conditions of elliptic curve Traces for FR-Reduction,” IEICE Trans. Fundamentals, vol. E84-A, no. 05, pp. 1234-1243, May 2001″, Reference literature 7: P. S. L. M. Barreto, B. Lynn, M. Scott, “Constructing elliptic curves with prescribed embedding degrees,” Proc. SCN 2002, LNCS 2576, pp. 257-267, Springer-Verlag, 2003″, Reference literature 8: R. Dupont, A. Enge, F. Morain, “Building curves with arbitrary small MOV degree over finite prime fields,” http://eprintiacr.org/2002/094).

It is well known that, based on the Chinese remainder theorem, there is an isomorphic map that maps from a factor ring Z/N_(φ)Z modulo composite number N_(φ)(N_(φ)=ω_(φ)−ι_(φ)) to the direct product of a residue field Z/ω_(φ)Z and a residue field Z/ι(_(φ)·ι_(φ)), and that there is an isomorphic map that maps from the direct map of a residue field Z/ω_(φ)Z and a residue field Z/ι_(φ)Z to a factor ring Z/N_(φ)Z (Reference literature 9: Johannes Buchmann “Introduction to Cryptography”, Springer Verlag Tokyo, (2001/07), ISBN-10: 4431708669 ISBN-13, pp. 52-56). That is, there are an isomorphic map HM_(φ) that maps a point on the first elliptic curve E_(φ,1) to a point on the second elliptic curve E_(φ,2) and a point on the third elliptic curve E_(φ,3), and its inverse map HM_(φ) ⁻¹. To take an example, an isomorphic map that maps an element K mod N_(φ) of a factor ring Z/N_(φ)Z to an element κ mod ω_(φ) of a residue field Z/ω_(φ)Z and an element κ mod ι_(φ) of a residue field Z/ι_(φ)Z can be HM_(φ) and a map that maps an element κ_(ω) mod ω_(φ) of a residue field Z/ω_(φ)Z and an element κ_(ι) mod ι_(φ) of a residue field Z/ι_(φ)Z to a an element κ_(ω)ι_(φ)ι_(φ)′+κ_(ι)ω_(φ)ω_(φ)′ mod N_(φ) of factor ring Z/N_(φ)Z can be HM_(φ) ⁻¹. Here, ω_(φ)′ and ι_(φ)′ are natural numbers that satisfy ω_(φ)ω_(φ)′+ι_(φ)ι_(φ)′=1. Such ω_(φ)′ and ι_(φ)′ can be easily generated by using the extended Euclidean algorithm. From the relation ω_(φ)ω_(φ)′+ι_(φ)ι_(φ)=1, application of HM_(φ) to κ_(ω)ι_(φ)ι_(φ)′+κ_(ι)ω_(φ)ω_(φ)′ mod N yields κ_(ω)ι_(φ)ι_(φ)′+κ_(ι)ω_(φ)ω_(φ)′ mod ω_(φ)=κ_(φ)ι_(φ)ι_(φ)′ mod ω_(φ)=κ_(ω)(1−ω_(φ)ω_(φ)′)mod ω_(φ)=κ_(φ) mod ω_(φ) ϵZ/ω _(φ) Z κ_(ω)ι_(φ)ι_(φ)′+κ_(ι)ω_(φ)ω_(φ)′ mod ι_(φ)=κ_(ι)ω_(φ)ω_(φ)′ mod ι_(φ)=κ_(ι)(1−ι_(φ)ι_(φ)′)mod ι_(φ)=κ_(ι) mod ι_(φ) ϵZ/ι _(φ) Z Thus, it can be seen that the mapping in this example is between HM_(φ) and HM_(φ) ⁻¹.

Therefore, if values resulting from factorization of the composite number N_(φ) into primes are given, that is, values of primes ω_(φ) and ι_(φ) are given, the determining unit 24111 can compute the bilinear map e_(φ)(α, β) on the first elliptic curve E_(φ,1) defined on the factor ring Z/N_(φ)Z by performing the following process of steps A to D.

(Step A) The determining unit 24111 uses the isomorphic map HM_(φ) to map a point αϵG_(φ,1) on the first elliptic curve E_(φ,1) defined on the factor ring Z/N_(φ)Z to a point θ_(ω)(α)ϵG_(φ,1ω) on the second elliptic curve E_(φ,2) defined on the residue field Z/ω_(φ)Z and a point θ₁(α)ϵG_(φ,1ι) on the third elliptic curve E_(φ,3) defined on the residue field Z/ι_(φ)Z.

(Step B) The determining unit 24111 uses the isomorphic map HM_(φ) to map a point βϵG_(φ,2) on the first elliptic curve E_(φ,1) defined on the factor ring Z/N_(φ)Z to a point θ₁₀₇(β)ϵG_(φ,2ω) on the second elliptic curve E_(φ,2) defined on the residue field Z/ω_(φ)Z and a point θ_(ι)(β)ϵG_(φ,2ι) on the third elliptic curve E_(φ,3) defined on the residue field Z/ι_(φ)Z.

(Step C) The determining unit 24111 obtains e_(φ,ω)(θ_(ω))(α), θ_(ω)(β)) and e_(φ,ι)(θ_(ι)(α), θ_(ι)(β)) on the second elliptic curve E_(φ,2) and the third elliptic curve E_(φ,3).

(Step D) The determining unit 24111 applies the inverse map HM_(φ) ⁻¹ to the obtained results of the computations e_(φ,ω)(θ_(ω)(α), θ_(ω)(β)) and e_(φ,ι)(θ_(ι)(α), θ_(ι)(β)) to obtain a value e_(φ)(α, β).

Thus, if values of primes ω_(φ) and ι_(φ) are given, the determining unit 24111 can compute e_(φ)(μ_(φ,g1), c_(φ,2u)), e_(φ)(c_(φ,1u), y(φ, 2)), e_(φ)(μ_(φ,g1), c_(φ,2v)), and e_(φ)(c_(φ,1v), y(φ, 2)) by following steps A through D to determine whether or not the relation e_(φ)(μ_(φ,g1), c_(φ,2u))/e_(φ)(c_(φ) y(φ, 2))=e_(φ)(μ_(φ,g1), c_(φ,2v))/e_(φ)(c_(φ,1v), y(φ, 2)) is satisfied.

On the other hand, no method for factorizing a large composite number N_(φ) into primes in polynomial time is known. Therefore, in this setting, the determining unit 24111 to which at least one of the primes ω_(φ) and ι_(φ) is not given cannot determine whether or not the relation e_(φ)(μ_(φ,g1), c_(φ,2u))/e_(φ)(c_(φ,1u), y(φ, 2))=e_(φ)(μ_(φ,g1), c_(φ,2v))/e_(φ)(c_(φ,1), y(φ, 2)) is satisfied.

Using the special groups G_(φ,1), G_(φ,2) and the bilinear map e_(φ) described above can allow only a person who knows at least one of the primes ω_(φ) and ι_(φ) to cause the computing apparatus 241-φ to perform the processes of steps S24107 and S24110.

Sixteenth Embodiment

A proxy computing system of a sixteenth embodiment is another example that embodies the first randomizable sampler and the second randomizable sampler described above. Specifically, the proxy computing system embodies an example of the first randomizable sampler and the second randomizable sampler in which H_(φ) is the direct product G_(φ) ×G_(φ) of groups G_(φ) which are cyclic groups, a function f_(φ)(x_(φ)) is a decryption function of the ElGamal encryption, that is, f_(φ)(c_(φ,1), c_(φ,2))=c_(φ,1)c_(φ,2) ^(−s(φ)) for an element x_(φ)=(c_(φ,1)c_(φ,2)) which is a ciphertext and a decryption key s(φ). The following description will focus on differences from the twelfth embodiment and description of commonalities with the twelfth embodiment will be omitted.

As illustrated in FIG. 31, the proxy computing system 205 of the sixteenth embodiment includes a computing apparatus 251-φ in place of the computing apparatus 211-φ and a capability providing apparatus 252 in place of the capability providing apparatus 212.

As illustrated in FIG. 32, the computing apparatus 251-φ of the sixteenth embodiment includes, for example, a natural number storage 21101-φ, a natural number selecting unit 21102-φ, an integer computing unit 21103-φ, an input information providing unit 25104-φ, a first computing unit 25105-φ, a first power computing unit 21106-φ, a first list storage 21107-φ, a second computing unit 25108-φ, a second power computing unit 21109-φ, a second list storage 21110-φ, a determining unit 21111-φ, a final output unit 21112-φ, and a controller 21113-φ. As illustrated in FIG. 36, the input information providing unit 25104-φ of this embodiment includes, for example, a fourth random number generator 25104 a-φ, a fifth random number generator 25104 b-φ, a first input information computing unit 25104 c-φ, a sixth random number generator 25104 d-φ, a seventh random number generator 25104 e-φ, and a second input information computing unit 25104 f-φ. The first input information computing unit 25104 c-φ includes, for example, a fourth input information computing unit 25104 ca-φ and a fifth input information computing unit 25104 cb-φ. The second input information computing unit 25104 f-φ includes, for example, a sixth input information computing unit 25104 fa-φ and a seventh input information computing unit 25104 fb-φ.

As illustrated in FIG. 33, the capability providing apparatus 252 of the sixteenth embodiment includes, for example, a first output information computing unit 25201, the second output information computing unit 25202, and a controller 21205.

<Processes>

Processes of this embodiment will be described below. In the sixteenth embodiment, it is assumed that a group H_(φ)=G_(φ)×G_(φ), an element x_(φ)=c_(φ,2))ϵH_(φ), f_(φ)(c_(φ,1), c_(φ,2)) is a homomorphic function, a generator of the group G_(φ) is μ_(φ,g), the order of the group G_(φ) is K_(φ,G), a pair of a ciphertext (V_(φ), W_(φ))ϵH_(φ) and a decrypted text f_(φ)(V_(φ), W_(φ))=Y_(φ)ϵG_(φ) decrypted from the ciphertext for the same decryption key s(φ) is preset in the computing apparatus 251-φ and the capability providing apparatus 252, and the computing apparatus 251-φ and the capability providing apparatus 252 can use the pair.

As illustrated in FIGS. 37 and 38, a process of the sixteenth embodiment is the same as the process of the twelfth embodiment except that steps S21103 through S21105, S21108, and S21200 through S21203 of the twelfth embodiment are replaced with steps S25103 through S25105, S25108, and S25200 through S25203, respectively. In the following, only processes at steps S25103 through S25105, S25108, and S25200 through S25203 will be described.

<<Process at Step S25103>>

The input information providing unit 25104-φ of the computing apparatus 251-φ (FIG. 32) generates and outputs first input information τ_(φ,1) corresponding to an input element x_(φ)=(c_(φ,1), c_(φ,2)) and second input information τ_(φ2) corresponding to the input element x_(φ)=(c_(φ,1), c_(φ,2)) (step S25103 of FIG. 37). A process at step S25103 of this embodiment will be described below with reference to FIG. 41.

The fourth random number generator 25104 a-φ (FIG. 36) generates a uniform random number r(φ, 4) that is a natural number greater than or equal to 0 and less than K_(φ,G). The generated random number r(φ, 4) is sent to the fourth input information computing unit 25104 ca-φ, the fifth input information computing unit 25104 cb-φ, and the first computing unit 25105-φ (step S25103 a). The fifth random number generator 25104 b-φ generates a uniform random number r(φ, 5) that is a natural number greater than or equal to 0 and less than K_(φ,G). The generated random number r(φ, 5) is sent to the fifth input information computing unit 25104 cb-φ and the first computing unit 25105-φ (step S25103 b).

The fourth input information computing unit 25104 ca-φ uses a natural number b(φ) selected by the natural number selecting unit 21102-φ, c_(φ,2) included in the element x_(φ)), and the random number r(φ, 4) to compute fourth input information C_(φ,2) ^(b(φ))W^(r(φ,4)) (step S25103 c). The fifth input information computing unit 25104 cb-φ uses the natural number b(φ) selected by the natural number selecting unit 21102-φ, c_(φ,1) included in the element x_(φ), and random numbers r(φ, 4) and r(q 5) to compute fifth input information c_(φ,1) ^(b(φ))V^(r(φ,4))μ_(φ,g) ^(r(φ,5)) (step S25103 d).

The sixth random number generator 25104 d-φ generates a uniform random number r(φ, 6) that is a natural number greater than or equal to 0 and less than K_(φ,G). The generated random number r(φ, 6) is sent to the sixth input information computing unit 25104 fa-φ, the seventh input information computing unit 25104 fb-φ, and the second computing unit 25108-φ (step S25103 e). The seventh random number generator 25104 e-φ generates a uniform random number r(φ, 7) that is a natural number greater than or equal to 0 and less than K_(φ,G). The generated random number r(φ, 7) is sent to the seventh input information computing unit 25104 fb-φ and the second computing unit 25108-φ (step S25103 f).

The sixth input information computing unit 25104 fa-φ uses a natural number a(φ) selected by the natural number selecting unit 21102-φ, c_(φ,2) included in the element x_(φ), and the random number r(φ, 6) to compute sixth input information c_(φ,2) ^(a(φ))W^(r(φ,6)) (step S25103 g). The seventh input information computing unit 25104 fb-φ uses the natural number a(φ) selected by the natural number selecting unit 21102-φ, c_(φ,1) included in the element x_(φ), and the random numbers r(φ, 6) and r(φ, 7) to compute seventh input information C_(φ,1) ^(a(φ))V^(r(φ,6))μ_(g) ^(r(φ,7)) (step S25103 h).

The first input information computing unit 25104 c-φ outputs the fourth input information c_(φ,2) ^(b(φ))W^(r(φ,4)) and the fifth input information c_(φ,1) ^(b(φ))>V_(φ) ^(r(φ,4))μ_(φ,g) ^(r(φ,5)) generated as described above as first input information τ_(φ,1)=(c_(φ,2) ^(b(φ))W_(φ) ^(r(φ,4)), c_(φ,1) ^(b(φ))>V_(φ) ^(r(φ,4))μ_(φ,g) ^(r(φ,5))). The second input information computing unit 25104 f-φ outputs the sixth input information c_(φ,2) ^(a(φ))W^(r(φ,6)) and the seventh input information c_(φ,1) ^(a(φ))V_(φ) ^(r(φ,6))μ_(φ,g) ^(r(φ,7)) generated as described above as second input information τ_(φ,2)=(c_(φ,2) ^(a(φ))W_(φ) ^(r(φ,6)), c_(φ,1) ^(a(φ))V_(φ) ^(r(φ,6))μ_(φ,g) ^(r(φ,7))) (step S25103 i).

<<Processes at Steps S25200 Through S25203>>

As illustrated in FIG. 38, first, the first input information τ_(φ1)=(c_(φ,2) ^(b(φ))W_(φ) ^(r(φ,4)), c_(φ,1) ^(b(φ))V_(φ) ^(r(φ,4))μ_(φ,g) ^(r(φ,5))) is input in the first output information computing unit 25201 of the capability providing apparatus 252 (FIG. 33) and the second input information τ_(φ,2)=(c_(φ,2) ^(a(φ))W^(r(φ,6)), c_(φ,1) ^(a(φ))V^(r(T′6))μ_(φ,g) ^(r(φ,7))) is input in the second output information computing unit 25202 (step S25200).

The first output information computing unit 25201 uses the first input information τ_(φ,1)=(c_(φ,2) ^(a(φ))W^(r(φ,4)), c_(φ,1) ^(a(φ))V^(r(T′4))μ_(φ,g) ^(r(φ,5))) and the decryption key s(φ) to correctly compute f_(φ)(c_(φ,1) ^(b(φ))V_(φ) ^(r(φ,4))μ_(φ,g) ^(r(φ,5)), c_(φ,2) ^(b(φ))W_(φ) ^(r(φ,4))) with a probability greater than a certain probability and sets the result of the computation as first output information z_(φ,1). The result of the computation may or may not be correct. That is, the result of the computation by the first output information computing unit 25201 may or may not be f_(φ)(c_(φ,1) ^(b(φ))V_(φ) ^(r(φ,4))μ_(φ,g) ^(r(φ,5)), c_(φ,2) ^(b(φ))W_(φ,4))) (step S25201).

The second output information computing unit 25202 can use the second input information τ_(φ,2)=(c_(φ,2) ^(a(φ))W^(r(φ,6)), c_(φ,1) ^(a(φ))V^(r(T′6))μ_(φ,g) ^(r(φ,7))) and the decryption key s(φ) to correctly compute f_(φ)(c_(φ,1) ^(a(φ))V_(φ) ^((φ,6))μ_(φ,g) ^(r(φ,7)), c_(φ,2) ^(a(φ))W_(φ) ^(r(φ,6))) with a probability greater than a certain probability and sets the result of the computation as second output information Z_(φ,2). The result of the computation may or may not be correct. That is, the result of the computation by the second output information computing unit 25202 may or may not be f_(φ)(c_(φ,1) ^(a(φ))V_(φ) ^(r(φ,6))μ_(φ,g) ^(r(φ,7)), c_(φ,2) ^(a(φ))W_(φ) ^(r(φ,6))) (step S25202).

The first output information computing unit 25201 outputs the first output information z_(φ,1) and the second output information computing unit 25202 outputs the second output information z_(φ,2) (step S25203).

<<Processes at Steps S25104 and S25105>>

Returning to FIG. 37, the first output information z_(φ,1) is input in the first computing unit 25105-φ of the computing apparatus 251-φ (FIG. 32) and the second output information z_(φ,2) is input in the second computing unit 25108-φ (step S25104).

The first computing unit 25105-φ uses the input first output information z_(φ,1) and the random numbers r(φ, 4) and r(φ, 5) to compute z_(φ,1) Y^(−r(φ,4))μ_(φ,g) ^(−r(φ,5)) and sets the result of the computation as u_(φ)(step S25105). The result u_(φ) of the computation is sent to the first power computing unit 21106-φ. Here, u_(φ)=z_(φ,4)Y_(φ) ^(−r(φ,4))μ_(φ,g) ^(−r(φ,5))=f_(φ)(c_(φ,1), c_(φ,2))^(b(φ))x_(φ,1). That is, z_(φ,4)Y_(φ) ^(−r(φ,4))μ_(φ,g) ^(−r(φ,5)) is an output of a randomizable sampler having an error X_(φ,1) for f_(φ)(c_(φ,1), c_(φ,2)). The reason will be described later.

<<Process at Step S25108>>

The second computing unit 25108-φ uses the input second output information z_(φ,2) and the random numbers r(φ,6) and r(φ,7) to compute z_(φ,2)Y_(φ) ^(−r(φ,6))μ_(φ,g) ^(−r(φ,7)) and sets the result of the computation as v_(φ). The result v_(φ) of the computation is sent to the second power computing unit 21109-φ. Here, v_(φ)=z_(φ,5)Y_(φ) ^(−r(φ,6))μ_(φ,g) ^(−r(φ,7))=f_(φ)(c_(φ,1), c_(φ,2))^(a(φ))x_(φ,2). That i s, z_(φ,5)Y_(φ) ^(−r(φ,6))μ_(φ,g) ^(−r(φ,7)) is an output of a randomizable sampler having an error X_(φ,2) for f_(φ)(c_(φ,1), c_(φ,2)). The reason will be described later.

<<Reason why z_(φ,4)Y_(φ) ^(−r(φ,4))μ_(φ,g) ^(−r(φ,5)) and z_(φ,5)Y_(φ) ^(−r(φ,6))μ_(φ,g) ^(−r(φ,7)) are Outputs of Randomizable Samplers Having Errors X_(φ,1) and X_(φ,2), Respectively, for f_(φ)(c_(φ,1), c_(φ,2))>>

Let c be a natural number, R_(I), R₂, R₁′ and R₂′ be random numbers, and B(c_(φ,1) ^(c)V_(φ) ^(R1)μ_(φ,g) ^(R2), c_(φ,2) ^(c)W_(φ) ^(R1)) be the result of computation performed by the capability providing apparatus 252 using c_(φ,1) ^(c)V_(φ) ^(R1)μ_(φ,g) ^(R2) and c_(φ,2) ^(c)W_(φ) ^(R1). That is, the first output information computing unit 25201 and the second output information computing unit 25202 return z_(φ)=B(c_(φ,1) ^(c)V_(φ) ^(R1)μ_(φ,g) ^(R2), c_(φ,2) ^(c)W_(φ) ^(R1)) as the results of computations to the computing apparatus 251-φ. Furthermore, a random variable X_(φ) having a value in a group G_(φ) is defined as X_(φ)=B(V_(φ) ^(R1′)μ_(φ,g) ^(R2′), W_(φ) ^(R1′))f_(φ)(V_(φ) ^(R1′)μ_(φ,g) ^(R2′), W_(φ) ^(R1′))⁻¹.

Here, z_(φ)Y_(φ) ^(−R1)μ_(φ,g) ^(−R2)=B(c_(φ,1) ^(c)V_(φ) ^(R1)μ_(φ,g) ^(R2), c_(φ,2) ^(c)W_(φ) ^(R1))Y_(φ) ^(−R1)μ_(φ,g) ^(−R2)=X_(φ)f_(φ)(c_(φ,1) ^(c)V_(φ) ^(R1)μ_(φ,g) ^(R2), c_(φ,2) ^(c)W_(φ) ^(R1))Y_(φ) ^(−R1)μ_(φ,g) ^(−R2)=X_(φ)f_(φ)(c_(φ,1), c_(φ,2))^(c)f_(φ)(v_(φ), W_(φ))^(R1)f_(φ)(μ_(φ,g), e_(φ,g))^(R2) Y_(φ) ^(−R1)μ_(φ,g) ^(−R2)=X_(φ)f_(φ)(c_(φ,1), c_(φ,2))^(c)Y_(φ) ^(R1)μ_(φ,g) ^(R2) μ_(φ,g) ^(−R1)μ_(φ,g) ^(−R2)=f_(φ)(c_(φ,1), c_(φ,2))^(c)X_(φ). That is, z_(φ)Y_(φ) ^(−R1)μ_(φ,g) ^(−R2) is an output of a randomizable sampler having an error X_(φ) for f_(φ)(x_(φ)). Note that e_(φ,g) is an identity element of the group G_(φ).

The expansion of formula given above uses the properties such that X_(φ)=B(V_(φ) ^(R1′)μ_(φ,g) ^(R2′), W_(φ) ^(R1′))f_(φ)(v_(φ) ^(R1′)μ_(φ,g) ^(R2′), W_(φ) ^(R1′))⁻¹=B(c_(φ,1) ^(c)V_(φ) ^(R1)μ_(φ,g) ^(R2), c_(φ2) ^(c)W_(φ) ^(R1))f_(φ)(c_(φ,1) ^(c)V_(φ) ^(R1)μ_(φ,g) ^(R2), c_(φ,2) ^(c)W_(φ) ^(R1)) and that B(c_(φ,1) ^(c)V_(φ) ^(R1)μ_(φ,g) ^(R2), c_(φ,2) ^(c)W^(R1))=X_(φ)f_(φ)(c_(φ,1) ^(c)V_(φ) ^(R1)μ_(φ,g) ^(R2), c_(φ,2) ^(c)W_(φ) ^(R1)). The properties are based on the fact that R₁, R₂, R₁′ and R₂′ are random numbers.

Therefore, considering that a(φ) and b(φ) are natural numbers and r(φ, 4), r(φ, 5), r(φ, 6), and r(φ, 7) are random numbers, Z_(φ,4)Y_(φ) ^(−r(φ,4))μ_(φ,g) ^(−r(φ,5)) and z_(φ,5)Y_(φ) ^(−r (φ,6))μ_(φ,g) ^(−r(φ,7)) are, likewise, outputs of randomizable samplers having errors X_(φ,1) and X_(φ,2), respectively, for f_(φ)(c_(φ,1), c_(φ,2)).

Seventeenth Embodiment

A proxy computing system of a seventeenth embodiment is another example that embodies the first randomizable sampler and the second randomizable sampler described above. Specifically, the proxy computing system embodies an example of a first randomizable sampler and a second randomizable sampler in which a group H_(φ) is the direct product H_(1,φ)×H_(2,φ) of cyclic groups H_(1,φ) and H_(2,φ), a generator of the cyclic group H_(1,φ) is η_(1,φ), a generator of the cyclic group H_(2,φ) is η_(2,φ), f_(φ) is a bilinear map that maps a pair of an element of the cyclic group H_(1,φ) and an element of the cyclic group H_(2,φ) to an element of a cyclic group G_(φ), an element x_(φ) of a cyclic group H_(φ) is a pair of an element λ_(1,φ) of the cyclic group H_(1,φ) and an element λ_(2,φ) of the cyclic group H_(2,φ), and Ω_(φ)=f_(φ)(η_(1,φ), η_(2,φ)). Examples of the bilinear map f_(φ) include functions and algorithms for computing pairings such as Weil pairing and Tate pairing. The following description will focus on differences from the twelfth embodiment and repeated description of commonalities with the twelfth embodiment will be omitted.

As illustrated in FIG. 31, the proxy computing system 207 of the seventeenth embodiment includes a computing apparatus 271-φ in place of the computing apparatus 211-φ and a capability providing apparatus 272 in place of the capability providing apparatus 212.

As illustrated in FIG. 32, the computing apparatus 271-φ of the seventeenth embodiment includes, for example, a natural number storage 21101-φ, a natural number selecting unit 21102-φ, an integer computing unit 21103-φ, an input information providing unit 27104-φ, a first computing unit 27105-φ, a first power computing unit 21106-φ, a first list storage 21107-φ, a second computing unit 27108-φ, a second power computing unit 21109-φ, a second list storage 21110-φ, a determining unit 27111-φ, a final output unit 21112-φ, and a controller 21113-φ. As illustrated in FIG. 42, the input information providing unit 27104-φ of this embodiment includes, for example, a first random number generator 27104 a-φ, a second random number generator 27104 c-φ, a first input information computing unit 27104 b-φ, and a second input information computing unit 27104 d-φ.

As illustrated in FIG. 33, the capability providing apparatus 272 of the seventeenth embodiment includes, for example, a first output information computing unit 27201, the second output information computing unit 27202, and a controller 21205.

<Processes>

Processes of this embodiment will be described below. In the seventeenth embodiment, it is assumed that a group H_(φ) is the direct product H_(1,φ)×H_(2,φ) of cyclic groups H_(1,φ) and H_(2,φ) a generator of the cyclic group H_(1,φ) is η_(1,φ), a generator of the cyclic group H_(2,φ) is η_(2,φ), f_(φ) is a bilinear map that maps a pair of an element of the cyclic group H_(1,φ) and an element of the cyclic group H_(2,φ) to an element of a cyclic group G_(φ), and an element x_(φ) of a group H_(φ) is a pair of an element λ_(1,φ)of the cyclic group H_(1,φ) and an element λ_(2,φ) of the cyclic group H_(2,φ), Ω_(φ)=f_(φ)(η_(1,φ), η_(2,φ)). Here, Ω_(φ)=f_(φ)(η_(1,φ), η_(2,φ)) is computed beforehand.

As illustrated in FIGS. 37 and 38, a process of the seventeenth embodiment is the same as the process of the twelfth embodiment except that steps S21103 through S21105, S21107, S21108, S21110, and S21200 through S21203 of the twelfth embodiment are replaced with steps S27103 through S27105, S27107, S27108, S27110, and S27200 through S27203, respectively. In the following, only processes at steps S27103 through S27105, S27107, S27108, S27110 and S27200 through S27203 will be described.

<<Process at Step S27103>>

The input information providing unit 27104-φ of the computing apparatus 271-φ (FIG. 32) generates and outputs first input information ι_(φ,1) corresponding to an input pair x_(φ)=(λ_(1,φ), λ_(2,φ)) of an element λ_(ι,φ) of the cyclic group H_(1,φ) and an element λ_(2,φ) of the cyclic group H_(2,φ) and second input information τ_(φ,2) corresponding to x_(φ)=(λ_(1,φ), λ_(2,φ)) (step S27103 of FIG. 37). A process at step S27103 of this embodiment will be described below with reference to FIG. 43.

The first random number generator 27104 a-φ (FIG. 42) generates uniform random numbers r(φ, 11), r(φ, 12), r(φ, 13), r(φ,14), r(φ, 15), and r(φ, 16) that are natural numbers greater than or equal to 0 and less than or equal to 2^(μ(k)+k). Here, μ(k) represents a function value of a security parameter k. The generated random numbers r(φ, 11), r(φ, 12), r(φ, 13), r(φ,14), r(φ, 15), and r(φ, 16) are sent to the first input information computing unit 27104 b-φ and the first computing unit 27105-φ (step S27103 a).

The first input information computing unit 27104 b-φ uses a natural number b(φ) selected by the natural number selecting unit 21102-φ, the input value x_(φ)=(λ_(1,φ), λ_(2,φ)), generators η_(1,φ) and η_(2,φ), and random numbers r(φ, 11), r(φ, 12), r(φ, 13), r(φ,14), r(φ, 15), and r(φ, 16) to compute (λ_(1,φ)η_(1,φ) ^(r(φ,11)·r(φ,12)), λ_(2,φ) ^(b(φ))η_(2,φ) ^(r(φ,13)·(φ,14))), (η_(1,φ) ^(r(φ,11)), λ_(2,φ) ^(−b(φ)·r(φ,12))η_(2,φ) ^(r(φ,15))) and (λ_(1,φ) ^(−r(φ,14))η_(1,φ) ^(r(φ,16)), η_(2,φ) ^(r(φ,13))) as first input information τ_(φ,1) (steps S27103 b through S27103 d).

The second random number generator 27104 c-φ (FIG. 42) generates uniform random numbers r(φ, 21), r(φ, 22), r(φ, 23), r(φ,24), r(φ, 25) and r(φ, 26) that are natural numbers greater than or equal to 0 and less than or equal to 2^(μ(k)+k). The generated random numbers r(φ, 21), r(φ, 22), r(φ, 23), r(φ,24), r(φ, 25) and r(φ, 26) are sent to the second input information computing unit 27104 d-φ and the second computing unit 27108-φ (step S27103 e).

The second input information computing unit 27104 d-φ uses a natural number a(φ) selected by the natural number selecting unit 21102-φ, the input value x_(φ)=(λ_(1,φ), λ_(2,φ)), the generators η_(1,φ) and r_(2,φ), and the random numbers r(φ, 21), r(φ, 22), r(φ, 23), r(φ,24), r(φ, 25) and r(φ, 26) to compute (λ_(1,φ)η_(1,φ) ^(r(φ,21)·r(φ,22)), λ_(2,φ) ^(a(φ))η_(2,φ) ^(r(φ,23)·r(φ,24))), (η_(1,φ) ^(r(φ,21)), λ_(2,φ) ^(−a(φ)·r(φ,22))η_(2,φ) ^(r(φ,25))) and (λ_(1,φ) ^(−r(φ,24))η_(1,φ) ^(r(φ,26)), η_(2,φ) ^(r(φ,23))) as second input information τ_(φ,2) (steps S27103 f through S27103 h).

The first input information computing unit 27104 b-φ outputs (λ_(1,φ)η_(1,φ) ^(r(φ,11)·r(φ,12)), λ_(2,φ)η_(2,φ) ^(r(φ,13)·r(φ,14)), (η_(1,φ) ^(r(φ,11)), λ_(2,φ) ^(−B(φ)·R(φ,12))η_(2,φ) ^(r(φ,15))), and (λ_(1,φ) ^(−r(φ,14))η_(1,φ) ^(r(φ,16)), η_(2,φ) ^(r(φ,13)) as the first input information τ_(φ,1). The second input information computing unit 27104 d-φ outputs λ_(1,φ)η_(1,φ) ^(r(φ,21)·r(φ,22)), λ_(2,φ) ^(a(φ))η_(2,φ) ^(r(φ,23)·r(φ,24)), (η_(1,φ) ^(r(φ,21)), λ_(2,φ) ^(−a(φ)·r(φ,22))ι_(2,φ) ^(r(φ,25))) and (λ_(1,φ) ^(−r(φ,24))η_(1,φ) ^(r(φ,26)), η_(2,φ) ^(r(φ,23))) as the second input information τ_(φ,2) (step S27103 i).

<<Processes at Steps S27200 Through S27203>>

As illustrated in FIG. 38, (λ_(1,φ)η_(1,φ) ^(r(φ,11)·r(φ,12)), λ_(2,φ) ^(b(φ))η_(2,φ) ^(r(φ,13)·(φ,14))), η_(1,φ) ^(r(φ,11)), λ_(2,φ) ^(−b(φ)·r(φ,12))η_(2,φ) ^(r(φ,15)), and (λ_(1,φ) ^(−r(φ,14))η_(1,φ) ^(r(φ,16)), η_(2,φ) ^(r(φ,14))), which are the first input information τ_(φ,1), are input in the first output information computing unit 27201 of the capability providing apparatus 272 (FIG. 33). (λ_(1,φ)η_(1,φ) ^(r(φ,21)·(φ,22)), λ_(2,φ) ^(a(φ))η_(2,φ) ^(r(φ,23)·r(φ,24))), (η_(1,φ) ^(r(φ,21)), λ_(2,φ) ^(−a(φ)·r(φ,22))η_(2,φ) ^(r(φ,25))) and (λ_(1,φ) ^(−r(φ,24))η_(1,φ) ^(r(φ,26)), η_(2,φ) ^(r(φ,23))), which are the second input information τ_(φ,2), are input in the second output information computing unit 27202 (step S27200).

The first output information computing unit 27201 uses the first input information τ_(φ,1) to correctly compute f_(φ)(λ_(1,φ)η_(1,φ) ^(r(φ,11)·r(φ,12)), λ_(2,φ) ^(b(φ))η_(2,φ) ^(r(φ,13)·(φ,14))), f_(φ)(η_(1,φ) ^(r(φ,11)), λ_(2,φ) ^(−b(φ)·r(φ,12))η_(2,φ) ^(r(φ,15))), and f_(φ)(λ_(1,φ) ^(−r(φ,14))η_(1,φ) ^(r(φ,16)), η_(2,φ) ^(r(φ,13))) with a probability greater than a certain probability and sets the obtained results of the computations, z_(φ,1,1), Z_(φ,1,2) and z_(φ,1,3), as first output information z_(φ,1). The results of the computations may or may not be correct (step S27201).

The second output information computing unit 27202 uses the second input information τ_(r,2) to correctly compute f_(φ)(λ_(1,φ)η_(1,φ) ^(r(φ,21)·r(φ,22)), λ_(2,φ) ^(a(φ))η_(2,φ) ^(r(φ,23)·(φ,24))), f_(φ)(η_(1,φ) ^(r(φ,21)), λ_(2,φ) ^(−a(φ)·r(φ,22))η_(2,φ) ^(r(φ,25))), and f_(φ)(λ_(1,φ) ^(−r(φ,24))η_(1,φ) ^(r(φ,26)), η_(2,φ) ^(r(φ,23))) with a probability greater than a certain probability and sets the obtained results of the computations, z_(φ,2,1), z_(φ,2,2) and Z_(φ,2,3), as second output information z_(φ,2). The results of the computations may or may not be correct (step S27202). The first output information computing unit 27201 outputs the first output information z_(φ,1) and the second output information computing unit 27202 outputs the second output information z_(φ,2) (step S27203).

<Processes at Steps S27104 and S27105>>

Returning to FIG. 37, the first output information z_(φ,1) is input in the first computing unit 27105-φ of the computing apparatus 271-φ (FIG. 32) and the second output information Z_(φ,2) is input in the second computing unit 27108-φ (step S27104).

The first computing unit 27105-φ uses the input first output information z_(φ,1)=(z_(φ,1,1), z_(φ,1,2), z_(φ,1,3)) and the random numbers r(φ, 11), r(φ, 12), r(φ, 13), r(φ,14), r(φ, 15), and r(φ, 16) to compute u_(φ)=Z_(φ,1,1)Z_(φ,1,2)Z_(φ,1,3)Ωz_(φ) ^(−r(φ,11)·r(φ,12)·r(φ,13)·r(φ,14)−r(φ,11)·r(φ,15)−r(φ,13)·r(φ,16)) to obtain the result u_(φ) of the computation (step S27105). The result u_(φ) of the computation is sent to the first power computing unit 21106-φ. Here, u_(φ)=f_(φ)(λ_(1,φ), λ_(2,φ))^(b(φ)))x_(φ,1). That is, z_(φ,1,1)z_(φ,1,2)z_(φ,1,3)Ωz_(φ) ^(−r(φ,11)·r(φ,12)·r(φ,13)·r(φ,14)−r(φ,11)·r(φ,15)−r(φ,13)·r(φ,16)) is an output of a randomizable sampler having an error X_(φ,1) for f_(φ)(λ_(1,φ), λ_(2,φ)). The reason will be described later.

<<Process at Step S27108>>

The second computing unit 27108-φ uses the input second output information z_(φ,2)=(z_(φ,2,1), z_(φ,2,2), z_(φ,2,3)) and the random numbers r(φ, 21), r(φ, 22), r(φ, 23), r(φ,24), r(φ, 25), and r(φ, 26) to compute v_(φ)=z_(φ,2,1)Z_(φ,2,2)z_(φ,2,3)Ωz_(φ) ^(−r(φ,21)·r(φ,22)·r(φ,23)·r(φ,24)−r(φ,21)·r(φ,25)−r(φ,23)·r(φ,26)) to obtain the result v_(φ) of the computation. The result v_(φ) of the computation is sent to the second power computing unit 21109-φ. Here, v_(φ)=f_(φ)(λ_(1,φ), λ_(2,φ))^(a(φ))x_(φ,2). That is, z_(φ,2,1)z_(φ,2,2)z_(φ,2,3)Ωz_(φ) ^(−r(φ,21)·r(φ,22)·r(φ,23)·r(φ,24)−r(φ,21)·r(φ,25)−r(φ,23)·r(φ,26)) is an output of a randomizable sampler having an error X_(φ,2) for f_(φ)(λ_(1,φ), λ_(2,y)). The reason will be described later.

<<Processes at Steps S27107 and S27110>>

At steps S27107 and S27110, the determining unit 27111-φ determines whether u_(φ)′=v_(φ)′. If it is determined at step S27107 that u_(φ)′=v_(φ)′, the process proceeds to step S21114; otherwise, the process proceeds to step S27108. If it is determined at step S27110 that u_(φ)′=v_(φ)′, the process proceeds to step S21114; otherwise, the process proceeds to step S21111.

<<Reason why z_(φ,1,1)z_(φ,1,1)z_(φ,1,3)Ωz_(φ) ^(−r(φ,11)·r(φ,12)·r(φ,13)·r(φ,14)−r(φ,11)·r(φ,15)−r(φ,13)·r(φ,16)) and z_(φ,2,1)z_(φ,2,2)z_(φ,2,3)Ωz_(φ) ^(−r(φ,21)·r(φ,22)·r(φ,23)·r(φ,24)−r(φ,21)·r(φ,25)−r(φ,23)·r(φ,26)) are output of randomizable samplers having errors X_(φ,1), and X_(φ,2), Respectively, for f_(φ)(λ_(1,φ), λ_(2,φ)))>>

Because of the bilinearity of f_(φ), the following relation holds for v_(φ). v _(φ) f _(φ)(λ_(1,φ),λ_(2,φ))^(−a(φ)) =z _(φ,2,1) z _(φ,2,2) z _(φ,2,3)Ω_(φ) ^(−r(φ,21)·r(φ,22)·r(φ,23)·r(φ,24)−r(φ,21)·r(φ,25)−r(φ,23)·r(φ,26)) f _(φ)(λ_(1,φ),λ_(2,φ))^(−a(φ)) =z _(φ,2,1) z _(φ,2,2) z _(φ,2,3) f _(φ)(λ_(1,φ)η_(1,φ) ^(−r(φ,21)·r(φ,22)),λ_(2,φ) ^(a(φ))η_(2,φ) ^(r(φ,23)·r(φ,24)−1) f _(φ)(λ_(1,φ)η_(1,φ) ^(r(φ,21)·(φ,22))λ_(2,φ) ^(a(φ))η_(2,φ) ^(r(φ,23)·r(φ,24)))Ω_(φ) ^(−r(φ,21)·r(φ,22)·r(φ,23)·r(φ,24)−r(φ,21)·r(φ,25)−r(φ,23)·r(φ,26)) f _(φ)(λ_(1,φ),λ_(2,φ))^(−a(φ)) =z _(φ,2,1) ′z _(φ,2,2) z _(φ,2,3) f _(φ)(λ_(1,φ)η_(1,φ) ^(−r(φ,21)·r(φ,22)),λ_(2,φ) ^(a(φ))η_(2,φ) ^(r(φ,23)·r(φ,24))Ω_(φ) ^(−r(φ,21)·r(φ,22)·r(φ,23)·r(φ,24)−r(φ,21)·r(φ,25)−r(φ,23)·r(φ,26)) f _(φ)(λ_(1,φ),λ_(2,φ))^(−a(φ)) =z _(φ,2,1) ′z _(φ,2,2) z _(φ,2,3) f _(φ)(λ_(1,φ)η_(1,φ) ^(−r(φ,21)·r(φ,22)),λ_(2,φ) ^(a(φ))η_(2,φ) ^(r(φ,23)·r(φ,24)) f _(φ)(η_(1,φ) ^(r(φ,21)),λ_(2,φ) ^(−a(φ)·r(φ,22))η_(2,φ) ^(r(φ,25)))f _(φ)(λ_(1,φ) ^(−(φ,24))η_(1,φ) ^(r(φ,26)),η_(2,φ) ^(r(φ,23)))Ω_(φ) ^(−r(φ,21)·r(φ,22)·r(φ,23)·r(φ,24)−r(φ,21)·r(φ,25)−r(φ,23)·r(φ,26)) f _(φ)(λ_(1,φ),λ_(2,φ))^(−a(φ)) =z _(φ,2,1) ′z _(φ,2,2) ′z _(φ,2,3)′

Here, z_(φ,2,1)′=z_(φ2,1)f_(φ)(λ_(1,φ)η_(1,φ) ^(−r(φ,21)·r(φ,22)),λ_(2,φ) ^(a(φ))η_(2,φ) ^(r(φ,23)·r(φ,24)))⁻¹, z_(φ,2,2)′=z_(φ,2,2)f_(φ)(η_(1,φ) ^(r(φ,21)), λ_(2,φ) ^(−a(φ)·r(φ,22))η_(2,φ) ^(r(φ,25))−1), and z_(φ,2,3)f_(φ)(λ_(1,φ) ^(−r(φ,24))η_(1,φ) ^(r(φ,26)), η_(2,φ) ^(r(φ,23)))⁻¹ are satisfied.

Each of z_(φ,2,1)′, z_(φ,2,2)′ and z_(φ,2,3)′ is statistically close to a probability distribution that is independent of a(φ). Accordingly, a probability distribution formed by v_(φ)f_(φ)(λ_(1,φ), λ_(2,φ))^(−a(φ)) is statistically close to a certain probability distribution X_(φ,2) that is independent of a(φ). Therefore, v_(φ) is an output of a randomizable sampler having an error X_(φ,2) for f_(φ)(λ_(1,φ), λ_(2,φ)). Similarly, u_(φ) is an output of a randomizable sampler having an error X_(φ,1) for f_(φ)(λ_(1,φ), λ_(2,φ)).

Eighteenth Embodiment

In the embodiments described above, a plurality of pairs (a(φ), b(φ)) of natural numbers a(φ) and b(φ) that are relatively prime to each other are stored in the natural number storage 21101-φ of the computing apparatus and the pairs (a(φ), b(φ)) are used to perform the processes. However, one of a(φ) and b(φ) may be a constant. For example, a(φ) may be fixed at 1 or b(φ) may be fixed at 1. In other words, one of the first randomizable sampler and the second randomizable sampler may be replaced with a sampler. If one of a(φ) and b(φ) is a constant, the process for selecting the constant a(φ) or b(φ) is unnecessary, a(φ) or b(φ) as a constant is not input in the processing units, and the processing units can treat it as a constant in computations. If a(φ) or b(φ) as a constant is equal to 1, f_(φ)(x_(φ))=u_(φ))^(b′(φ))v_(φ) ^(,a′(φ)) can be obtained as f_(φ)(x_(φ))=v_(φ) or f_(φ)(x_(φ))=u_(φ) without using a′(φ) or b′(φ).

An eighteenth embodiment is an example of such a variation, in which b(φ) is fixed at 1 and the second randomizable sampler is replaced with a sampler. The following description will focus on differences from the twelfth embodiment. Specific examples of the first randomizable sampler and the sampler are similar to those described in the thirteenth to seventeenth embodiments and therefore description of the first randomizable sampler and the sampler will be omitted.

<Configuration>

As illustrated in FIG. 31, a proxy computing system 206 of the eighteenth embodiment includes a computing apparatus 261-φ in place of the computing apparatus 211-φ of the twelfth embodiment and a capability providing apparatus 262 in place of the capability providing apparatus 212.

As illustrated in FIG. 44, the computing apparatus 261-φ of the eighteenth embodiment includes, for example, a natural number storage 26101-φ, a natural number selecting unit 26102-φ, an input information providing unit 26104-φ, a first computing unit 26105-φ, a first power computing unit 21106-φ, a first list storage 21107-φ, a second computing unit 26108-φ, a second list storage 26110-φ, a determining unit 26111-φ, a final output unit 21112-φ, and a controller 21113-φ.

As illustrated in FIG. 33, the capability providing apparatus 262 of the eighteenth embodiment includes, for example, a first output information computing unit 26201, a second output information computing unit 26202, and a controller 21205.

<Assumptions for Processes>

No natural number b(φ) is stored in the natural number storage 26101-φ of the computing apparatus 261-φ and only a plurality of natural numbers a(φ) are stored. The rest of the assumptions are the same as those in any of the twelfth to seventeenth embodiments.

<Processes>

As illustrated in FIG. 45, first, the natural number selecting unit 26102-φ of the computing apparatus 261-φ (FIG. 44) randomly reads one natural number a(φ) from among the plurality of natural numbers a(φ) stored in the natural number storage 26101-φ. Information on the read natural number a(φ) is sent to the input information providing unit 26104-φ and the first power computing unit 21106-φ (step S26100).

The controller 21113-φ sets t=1 (step S21102).

The input information providing unit 26104-φ generates and outputs first input information τ_(φ,1) and second input information τ_(φ,2) each of that corresponds to an input element x_(φ). Preferably, the first input information τ_(φ,1) and the second input information τ_(φ,2) are information whose relation with the element x_(φ) is scrambled. This enables the computing apparatus 261-φ to conceal the element x_(φ) from the capability providing apparatus 262. Preferably, the second input information τ_(φ,2) of this embodiment further corresponds to the natural number a(φ) selected by the natural number selecting unit 26102-φ. This enables the computing apparatus 261-φ to evaluate the computation capability provided by the capability providing apparatus 262 with a high degree of accuracy (step S26103). A specific example of a pair of the first input information τ_(φ,1) and the second input information τ_(φ,2) is a pair of first input information τ_(φ,1) and second input information τ_(φ,2) when b(φ)=1 in any of the thirteenth to seventeenth embodiments.

As illustrated in FIG. 38, the first input information τ_(φ,1) is input in the first output information computing unit 26201 of the capability providing apparatus 262 (FIG. 33) and the second input information τ_(φ,2) is input in the second output information computing unit 26202 (step S26200).

The first output information computing unit 26201 uses the first input information τ_(φ,1) to correctly compute f_(φ)(τ_(φ,1)) with a probability greater than a certain probability and sets the obtained result of the computation as first output information z_(φ,1) (step S26201). The second output information computing unit 26202 uses the second input information τ_(φ,2) to correctly compute f_(φ)(τ_(φ,2)) with a probability greater than a certain probability and sets the obtained result of the computation as second output information z_(φ,2) (step S26202). That is, the first output information computing unit 26201 and the second output information computing unit 26202 can output computation results that have an intentional or unintentional error. In other words, the result of the computation by the first output information computing unit 26201 may or may not be f_(φ)(τ_(φ,1)) and the result of the computation by the second output information computing unit 26202 may or may not be f_(φ)(τ_(φ,2)). A specific example of a pair of the first output information z_(φ,1) and the second output information z_(φ,2) is a pair of first output information z_(φ,1) and second output information z_(φ,2) when b(φ)=1 in any of the thirteenth to seventeenth embodiments.

The first output information computing unit 26201 outputs the first output information z_(φ,1) and the second output information computing unit 26202 outputs the second output information z_(φ,2) (step S26203).

Returning to FIG. 45, the first output information z_(φ,1) is input in the first computing unit 26105-φ of the computing apparatus 261-φ (FIG. 44) and the second output information z_(φ,2) is input in the second computing unit 26108-φ. The first output information z_(φ,1) and the second output information z_(φ,2) are equivalent to the computation capability provided by the capability providing apparatus 262 to the computing apparatus 261-φ (step S26104).

The first computing unit 26105-φ generates a computation result u_(φ)=f_(φ)(x_(φ))x_(φ,1) from the first output information z_(φ,1). A specific example of the result u_(φ) of the computation is the result u_(φ) of the computation in any of the thirteenth to seventeenth embodiments when b(φ)=1. The result u_(φ) of the computation is sent to the first power computing unit 21106-φ (step S26105).

The first power computing unit 21106-φ computes u_(φ)′=u_(φ) ^(a(φ)). The pair of the result u_(φ) of the computation and u_(φ)′ computed on the basis of the result of the computation, (u_(φ), u_(φ)′), is stored in the first list storage 21107-φ (step S21106).

The second computing unit 26108-φ generates a computation result v_(φ)=f_(φ)(x_(φ))^(a(φ)) _(φ,2) from the second output information z_(φ,2). A specific example of the result v_(φ) of the computation is the result v_(φ) of the computation in any of the thirteenth to seventeenth embodiments. The result v_(φ) of the computation is stored in the second list storage 26110-φ (step S26108).

The determining unit 26111-φ determines whether or not there is a pair of u_(φ)′ and v_(φ) that belong to a class CL_(φ)(M_(φ)) corresponding to the same element M_(φ) among the pairs (u_(φ), u_(φ)′) stored in the first list storage 21107-φ and v_(φ) stored in the second list storage 26110-φ as in any of the twelfth to seventeenth embodiments (step S26110). If there is a pair of u_(φ)′ and v_(φ) that belong to the class CL_(φ)(M_(φ)) corresponding to the same M_(φ), the process proceeds to step S26114. If there is not a pair of u_(φ)′ and v_(φ) that belong to the class CL_(φ)(M_(φ)) corresponding to the same element M_(φ), the process proceeds to step S21111.

At step S21111, the controller 21113-φ determines whether or not t=T (step S21111). T is a predetermined natural number. If t=T, the controller 21113-φ outputs information indicating that the computation is impossible, for example the symbol “⊥” (step S21113) and the process ends. If not t=T, the controller 21113-φ increments t by 1, that is, sets t=t+1 (step S21112) and the process returns to step S26103.

At step S26114, the final output unit 21112-φ outputs u_(φ) corresponding to u_(φ)′ included in the pair of u_(φ)′ and v_(φ) determined to belong to the class CL_(φ)(M_(φ)) corresponding to the same element M_(φ), (step S26114). The u_(φ) thus obtained corresponds to u_(φ) ^(b′(φ))v_(φ) ^(a′(φ)) in the twelfth to seventeenth embodiments when b(φ)=1. That is, u_(φ) thus obtained can be f_(φ)(x_(φ)) with a high probability. Therefore, a predetermined reliability that the selected u_(φ) is equal to f_(φ)(x_(φ)) or higher can be achieved by repeating at least the process described above multiple times and selecting the value u_(φ) obtained with the highest frequency among the values obtained at step S26114. As will be described, u_(φ)=f_(φ)(x_(φ)) can result with an overwhelming probability, depending on settings.

<<Reason why f_(φ)(x_(φ)) can be Obtained>>

The reason why a decryption result f_(φ)(x_(φ)) can be obtained on the computing apparatus 261-φ of this embodiment will be describe below. Terms required for the description will be defined first.

Black-Box:

A black-box F_(φ)(τ_(φ)) of f_(φ)(τ_(φ)) is a processing unit that takes an input of τ_(φ)ϵH_(φ) and outputs z_(φ)ϵG_(φ). In this embodiment, each of the first output information computing unit 26201 and the second output information computing unit 26202 is equivalent to the black box F_(φ)(τ_(φ)) for the decryption function f_(φ)(τ_(φ)). A black-box F_(φ)(τ_(φ)) that satisfies z_(φ)=f_(φ)(τ_(φ)) for elements τ_(φ)ϵ_(U)H_(φ) and z_(φ)=F_(φ)(τ_(φ))arbitrarily selected from a group H_(φ) with a probability greater than δ (0<δ≤1), that is, a black-box F_(φ)(τ_(φ)) for f_(φ)(τ_(φ)) that satisfies Pr[z _(φ) =f _(φ)(τ_(φ))|τ_(φ)ϵ_(U) H _(φ) ,z _(φ) =F _(φ)(τ_(φ))]>δ  (15) is called the δ-reliable black-box F_(φ)(τ_(φ)) for f_(φ)(τ_(φ)). Here, δ is a positive value and is equivalent to the “certain probability” mentioned above.

Self-Corrector:

A self-corrector C^(F)(x_(φ)) is a processing unit that takes an input of x_(φ)ϵH_(φ), performs computation by using a black-box F_(φ)(τ_(φ)) for f_(φ)(τ_(φ)) and outputs jϵG∪⊥. In this embodiment, the computing apparatus 261-φ is equivalent to the self-corrector C^(F)(x_(φ)).

Almost Self-Corrector:

Assume that a self-corrector C^(F)(x_(φ)) that takes an input of x_(φ)ϵH_(φ) and uses the δ-reliable black-box F_(φ)(τ_(φ)) for f_(φ)(τ_(φ)) outputs a correct value j=f_(φ)(x_(φ)) with a probability sufficiently greater than the provability with which the self-corrector C^(F)(x_(φ)) outputs an incorrect value j≠f_(φ)(x_(φ)). That is, assume that a self-corrector C^(F)(x_(φ)) satisfies Pr[j=f _(φ)(x _(φ))|j=C ^(F)(x _(φ)),j≠⊥]>Pr[j≠f _(φ)(x _(φ))|j=C ^(F)(x _(φ)),j≠⊥]+Δ   (16) Here, Δ is a certain positive value (0<Δ<1). If this is the case, the self-corrector C^(F)(x_(φ)) is called an almost self-corrector. For example, for a certain positive value Δ′ (0<Δ′<1), if a self-corrector C^(F)(x_(φ)) satisfies Pr[j=f _(φ)(x _(φ))|j=C ^(F)(x _(φ))]>(⅓)+Δ′ Pr[j=⊥|j=C ^(F)(x _(φ))]<⅓ Pr[j≠f _(φ)(x _(φ)) and j≠⊥|j=C ^(F)(x _(φ))]<⅓, then the self-corrector C^(F)(x_(φ)) is an almost self-corrector. Examples of Δ′ include Δ′= 1/12 and Δ′=⅓.

Robust Self-Corrector:

Assume that a self-corrector C^(F)(x_(φ)) that takes an input of x_(φ)ϵH and uses a δ-reliable black-box F_(φ)(τ_(φ)) for f_(φ)(x_(φ)) to output a correct value j=f_(φ)(x_(φ)) or j=⊥ with an overwhelming probability. That is, assume that for a negligible error ξ (0≤ξ<1), a self-corrector C^(F)(x_(φ)) satisfies Pr[j=f _(φ)(x _(φ)) or j=⊥|j=C ^(F)(x _(φ))]>1−ξ  (17) If this is the case, the self-corrector C^(F)(x_(φ)) is called a robust self-corrector. An example of the negligible error ξ is a function vale ξ(k) of a security parameter k. An example of the function value ξ(k) is a function value ξ(k) such that {ξ(k)p(k)} converges to 0 for a sufficiently large k, where p(k) is an arbitrary polynomial. Specific examples of the function value ξ(k) include ξ(k)=2^(−k) and ξ(k)=2^(−√k).

A robust self-corrector can be constructed from an almost self-corrector. Specifically, a robust self-corrector can be constructed by executing an almost self-constructor multiple times for the same x and selecting the most frequently output value, except ⊥, as j. For example, an almost self-corrector is executed O(log(1/ξ)) times for the same x and the value most frequently output is selected as j, thereby a robust self-corrector can be constructed. Here, O(⋅) represents O notation.

Pseudo-Free Action:

An upper bound of the probability Pr[α _(φ) ^(a(φ))=β_(φ) and α_(φ) ≠e _(φ,g) |a(φ)ϵ_(U)Ω,α_(φ) ϵX _(φ,1),β_(φ) ϵX _(φ,2)]   (18) of satisfying α_(φ) ^(a(φ))=β_(φ) for all possible X_(φ,1) and X_(φ,2) is called a pseudo-free indicator of a pair (G_(φ), Ω_(φ)) and is represented as P(G_(φ), Ω_(φ)), where G_(φ) is a group G_(φ), Ω_(φ) is a set of natural numbers Ω_(φ)={0, . . . , M_(φ)} (M_(φ) is a natural number greater than or equal to 1), α_(φ) and β_(φ) are realizations α_(φ)ϵX_(φ,1) (α_(φ)≠e_(φ,g)) and β_(φ)ϵX_(φ,2) of random variables X_(φ,1) and X_(φ,2) that have values in the group G_(φ), and a(φ)ϵΩ_(φ). If a certain negligible function ζ(k) exists and P(G _(φ),Ω_(φ))<ζ(k)  (19), then a computation defined by the pair (G_(φ), Ω_(φ)) is called a pseudo-free action. Note that “α_(φ) ^(a(φ))” means that a computation defined on the group G_(φ) is applied a(φ) times to α_(φ). An example of the negligible function ζ(k) is such that {ζ(k)p(k)} converges to 0 for a sufficiently large k, where p(k) is an arbitrary polynomial. Specific examples of the function ζ(k) include ζ(k)=2^(−k) and ζ(k)=2^(−√k). For example, if the probability of Formula (18) is less than O(2^(−k)) for a security parameter k, a computation defined by the pair (G_(φ), Ω_(φ)) is a pseudo-free action. For example, if the number of the elements |Ω_(φ)·α_(φ)| of a set Ω_(φ)·α_(φ)={a(φ)(α_(φ))|a(φ)ϵΩ_(φ)} exceeds 2^(k) for any α_(φ)ϵG_(φ) where α_(φ)≠e_(φ,g) a computation defined by the pair (G_(φ), Ω_(φ)) is a pseudo-free action. Note that a(φ)(a_(φ)) represents the result of a given computation on a(φ) and α_(φ). There are many such examples. For example, if the group G_(φ) is a residue group Z/pZ modulo prime p, the prime p is the order of 2^(k) the set Ω_(φ)={0, . . . p−2}, a(φ)(α_(φ)) is α_(φ) ^(a(φ))ϵZ/pZ, and α_(φ)≠e_(φ,g), then Ω_(φ)·α_(φ)={α_(φ) ^(a(φ))|a(φ)=0, . . . p−2}={e_(φ,g), α_(φ) ¹, . . . , α_(φ) ^(p-2)} and |Ω_(φ)·α_(φ)|=p−1. If a certain constant C exists and k is sufficiently large, |Ω_(φ)·α_(φ)|>C2^(k) is satisfied because the prime p is the order of 2^(k). Here, the probability of Formula (18) is less than C⁻¹2^(−k) and a computation defined by such pair (G_(φ), Ω_(φ)) is a pseudo-free action.

δ^(γ)-Reliable Randomizable Sampler:

A randomizable sampler that whenever a natural number a(φ) is given, uses the black-box F_(φ)(τ_(φ)) for δ-reliable f_(φ)(τ_(φ)) and returns w_(φ) ^(a(φ))x_(φ)′ corresponding to a sample x_(φ)′ that depends on a random variable X_(φ) for w_(φ)ϵG_(φ), where the probability that w_(φ) ^(a(φ))x_(φ)′=w_(φ) ^(a(φ)) is greater than δ^(γ) (γ is a positive constant), that is, Pr[w _(φ) ^(a(φ)) x _(φ) ′=w _(φ) ^(a(φ))]>δ^(γ)  (20) is satisfied, is called a δ^(γ)-reliable randomizable sampler. The combination of the input information providing unit 26104-φ, the second output information computing unit 26202, and the second computing unit 26108-φ of this embodiment is a δ^(γ)-reliable randomizable sampler for w_(φ)=f_(φ)(x_(φ)).

The definitions given above will be used to describe the reason why f_(φ)(x_(φ)) can be obtained on the computing apparatus 261-φ. At step S26110 of this embodiment, determination is made as to whether there is a pair of u_(φ)′ and v_(φ) that belong to a class corresponding to the same element, that is, whether there is a pair of u_(φ) ^(a(φ)) and v_(φ) that belong to a class corresponding to the same element. Since the combination of the input information providing unit 26104-φ, the second output information computing unit 26202, and the second computing unit 26108-φ is a δ^(γ)-reliable randomizable sampler (Formula (20)), u_(φ) ^(a(φ)) and v_(φ) belong to a class CL_(φ)(M_(φ)) corresponding to the same element M_(φ) with an asymptotically large probability if T is a large value greater than a certain value determined by k, δ and γ (Yes at step S26110). For example, Markov's inequality can be used to show that if T≥4/δ^(γ), the probability that u_(φ) ^(a(φ)) and v_(φ) belong to a class corresponding to the same element (Yes at step S26110) is greater than ½.

Since u_(φ)=f_(φ)(x_(φ))x_(φ,1) and v_(φ)=f_(φ)(x_(φ))^(a(φ))x_(φ,2) in this embodiment, u_(φ) ^(a(φ))=v_(φ) is satisfied and x_(φ,1) ^(a(φ))X_(φ,2) holds if the determination at step S26110 is yes, provided that the function f_(φ)(x_(φ)) is an injective function for the element x_(φ). Even where the function f_(φ)(x_(φ)) is not an injective function for an element x_(φ), x_(φ,1) ^(a(φ))=x_(φ,2) holds if the determination at step S26110 is yes, provided that f_(φ)(x_(φ)) is a homomorphic function.

x_(φ,1) ^(a(φ))=x_(φ,2) holds if x_(φ,1)=x_(φ,2)=e_(φ,g) or x_(φ,1)≠e_(φ,g). If x_(φ,1)=x_(φ,2)=e_(φ,g), then u_(φ)=f_(φ)(x_(φ)) and therefore u_(φ) output at step S26114 is a correct decryption result f_(φ)(x_(φ)). On the other hand, if x_(φ,1)≠e_(φ,g), then u_(φ)≠f_(φ)(x_(φ)) and therefore u_(φ) output at step S26114 is not a correct decryption result f_(φ)(x_(φ)).

If an computation defined by a pair (G_(φ), Ω_(φ)) of a group G_(φ) and a set Ω_(φ) to which a natural number a(φ) belongs is pseudo-free action or T²P(G_(φ), Ω_(φ)) is asymptotically small for a pseudo-free index P(G_(φ), Ω_(φ)), the probability that x_(φ)≠e_(φ,g) when x_(φ,1) ^(a(φ))=x_(φ,2) (Formula (18)) is asymptotically small. Accordingly, the probability that x_(φ,1)=e_(φ,g) when x_(φ,1) ^(a(φ))=x_(φ,2) is asymptotically large. Therefore, if an computation defined by a pair (G_(φ), Ω_(φ)) is a pseudo-free action or T²P(G_(φ), Ω_(φ)) is asymptotically small, the probability that an incorrect decryption result f_(φ)(x_(φ)) is output when u_(φ) ^(a(φ)) and v_(φ) belong to the class CL_(φ)(M_(φ)) corresponding to the same element M_(φ) is sufficiently smaller than the probability that a correct decryption result f_(φ)(x_(φ)) is output when and and v_(φ) belong to the class CL_(φ)(M_(φ)) corresponding to the same element M_(φ). In this case, it can be said that the computing apparatus 261-φ is an almost self-corrector (see Formula (16)). Therefore, a robust self-corrector can be constructed from the computing apparatus 261-φ as described above and a correct decryption result f_(φ)(x_(φ)) can be obtained with an overwhelming probability. If a computation defined by (G_(φ), Ω_(φ)) is a pseudo-free action, the probability that an incorrect decryption result f_(φ)(x_(φ)) is output when u_(φ) ^(a(φ)) and v_(φ) belong to the class CL_(φ)(M_(φ)) corresponding to the same element M_(φ) is also negligible. In that case, the computing apparatus 261-φ outputs a correct decryption result f_(φ)(x_(φ)) or ⊥ with an overwhelming probability.

Note that “η(k′) is asymptotically small” means that k₀ is determined for an arbitrary constant ρ and the function value η(k′) for any k′ that satisfies k₀<k′ for k₀ is less than ρ. An example of k′ is a security parameter k. “η(k′) is asymptotically large” means that k₀ is determined for an arbitrary constant ρ and the function value 1−η(k′) for any k′ that satisfies k₀<k′ for k₀ is less than ρ.

The proof given above also proves that “if u_(φ)′ and v_(φ)′ belong to the class CL_(φ)(M_(φ)) corresponding to the same element M_(φ), it is highly probable that the first randomizable sampler has correctly computed u_(φ)=f_(φ)(x_(φ))^(b((φ)) and the second randomizable sampler has correctly computed v_(φ)=f_(φ)(x_(φ))^(a(φ)) (x_(φ,1) and x_(φ,2) are identity elements e_(φ,g) of the group G_(M))” stated in the twelfth embodiment, as can be seen by replacing a(φ) with a(φ)/b(φ).

<<δ^(γ)-Reliable Randomizable Sampler and Security>>

Consider the following attack.

-   -   A black-box F _(φ)(τ_(φ)) or part of the black-box F _(φ)(τ_(φ))         intentionally outputs an invalid z_(φ) or a value output from         the black-box F_(φ)(τ_(φ)) is changed to an invalid z_(φ).     -   w_(φ) ^(a(φ))x_(φ)′ corresponding to the invalid z_(φ) is output         from the randomizable sampler.     -   w_(φ) ^(a(φ))x_(φ)′ corresponding to the invalid z_(φ) increases         the probability with which the self-corrector C^(F)(x_(φ))         outputs an incorrect value even though w_(φ) ^(a(φ))x_(φ)′         corresponding to the invalid z_(φ) causes the self-corrector         C^(F)(x_(φ)) to determine that u_(φ) ^(a(φ)) and v_(φ) belong to         the class CL_(φ)(M_(φ)) corresponding to the same M_(φ)(Yes at         step S26110).

Such an attack is possible if the probability distribution D_(a)=w_(φ) ^(a(φ))x_(φ)′w_(φ) ^(−a(φ)) of an error of output from the randomizable sampler for a given natural number a(φ) depends on the natural number a(φ). For example, if tampering is made so that v_(φ) output from the second computing unit 26108-φ is f_(φ)(x_(φ))^(a(φ))x_(φ,1) ^(a(φ)), always u_(φ) ^(a(φ))=v_(φ) holds and it is determined that u_(φ) ^(a(φ)) and v_(φ) belong to the class CL_(φ)(M_(φ)) corresponding to the same M_(φ) regardless of the value of Therefore it is desirable that the probability distribution D_(a)=w_(φ) ^(a(φ))x_(φ)′w_(φ) ^(−a(φ)) of an error of w_(φ) ^(a(φ))x_(φ)′ output from the randomizable sampler for a given natural number a(φ) do not depend on the natural number a(φ).

Alternatively, it is desirable that the randomizable sampler be such that there is a probability distribution D that has a value in a group G_(φ) and is indistinguishable from the probability distribution D_(a)=w_(φ) ^(a(φ))x_(φ)′w_(φ) ^(−a(φ)) of an error of w_(φ) ^(a(φ))x_(φ)′ for any element a(φ)ϵ^(∀)Ω_(φ) of a set Ω_(φ)(the probability distribution D_(a) and the probability distribution D are statistically close to each other). Note that the probability distribution D is not dependent on the natural number a(φ). That the probability distribution D_(a) and the probability distribution D are indistinguishable from each other means that the probability distribution D_(a) and the probability distribution D cannot be distinguished from each other by a polynomial time algorithm. For example, if Σ_(gϵG) |Pr[gϵD]Pr[gϵD _(a)]|<ζ  (21) is satisfied for negligible ζ (0≤ζ1), the probability distribution D_(a) and the probability distribution D cannot be distinguished from each other by a polynomial time algorithm. An example of negligible ζ is a function value ζ(k) of the security parameter k. An example of the function value ζ(k) is a function value such that {ζ(k)p(k)} converges to 0 for a sufficiently large k where p(k) is an arbitrary polynomial. Specific examples of the function ζ(k) include ζ(k)=2^(−k) and ζ(k)=2^(−√k). These also apply to the twelfth to seventeenth embodiments which use natural numbers a(φ) and b(φ).

[Variations of the Twelfth to Eighteenth Embodiments]

In a variation of the twelfth to eighteenth embodiments, even though it is not guaranteed that the capability providing apparatus always performs correct computations, a value u_(φ) ^(b′(φ))v_(φ) ^(a′(φ)) obtained on each of the computing apparatuses φ will be f_(φ)(x_(φ)) with a high probability when the capability providing apparatus correctly computes f_(φ)(τ_(φ,1)) and f_(φ)(τ_(φ,2)) with a probability greater than a certain probability. Therefore, each of the computing apparatuses φ can cause the capability providing apparatus to perform a computation without performing authentication and can obtain a correct result of the computation (for example, the result of decryption of a ciphertext) by using the result of the computation.

The present invention is not limited to the embodiments described above. For example, random variables X_(φ,1), X_(φ,2) and X_(φ,3) may or may not be the same.

Each of the random number generators generates uniform random numbers to increase the security of the proxy computing system to the highest level. However, if the level of security required is not so high, at least some of the random number generators may generate random numbers that are not uniform random numbers. While it is desirable from the computational efficiency point of view that random natural numbers selected are natural numbers greater than or equal to 0 and less than K_(φ,H) or natural numbers greater than or equal to 0 and less than or equal to 2^(μ(k)+k) in the embodiments described above, random numbers that are natural numbers greater than or equal to K_(φ,II) or natural numbers greater than 2^(μ(k)+k) may be selected instead. Here, μ is a function of k. For example, μ may be the length of an element of the group H_(φ) as a bit string.

The process of the capability providing apparatus may be performed multiple times each time the computing apparatus provides first input information τ_(φ,1) and second input information τ_(φ,2) corresponding to the same a(φ) and b(φ) to the capability providing apparatus. This enables the computing apparatus to obtain a plurality of pieces of first output information z_(φ,1), second output information z_(φ,2), and third output information z_(φ,3) each time the computing apparatus provides first input information τ_(φ,1), and second input information τ_(φ,2) to the capability providing apparatus. Consequently, the number of exchanges and the amount of communication between the computing apparatus and the capability providing apparatus can be reduced.

The computing apparatus may provide a plurality of pieces of the first input information τ_(φ,1) and the second input information φ_(φ,2) to the capability providing apparatus at once and may obtain a plurality of pieces of corresponding first output information z_(φ,1), second output information z_(φ,2) and third output information z_(φ,3) at once. This can reduce the number of exchanges between the computing apparatus and the capability providing apparatus.

The units of the computing apparatus may exchange data directly or through a memory, which is not depicted. Similarly, the units of the capability providing apparatus may exchange data directly or through a memory, which is not depicted.

Check may be made to see whether u_(φ) and v_(φ) obtained at the first computing unit and the second computing unit of any of the embodiments are elements of the group G_(φ). They are elements of the group G_(φ), the process described above may be continued; if u_(φ) or v_(φ) is not an element of the group G_(φ), information indicating that the computation is impossible, for example the symbol “⊥”, may be output.

Furthermore, the processes described above may be performed not only in time sequence as is written or may be performed in parallel with one another or individually, depending on the throughput of the apparatuses that perform the processes or requirements. It would be understood that other modifications can be made without departing from the spirit of the present invention.

If any of the configurations described above is implemented by a computer, the processes of the functions the apparatuses need to include are described by a program. The processes of the functions are implemented on the computer by executing the program on the computer. The program describing the processes can be recorded on a computer-readable recording medium. An example of the computer-readable recording medium is a non-transitory recording medium. Examples of such a recording medium include a magnetic recording device, an optical disc, a magneto-optical recording medium, and a semiconductor memory.

The program is distributed by selling, transferring, or lending a portable recording medium on which the program is recorded, such as a DVD or a CD-ROM. The program may be stored on a storage device of a server computer and transferred from the server computer to other computers over a network, thereby distributing the program

A computer that executes the program first stores the program recorded on a portable recording medium or transferred from a server computer into a storage device of the computer. When the computer executes the processes, the computer reads the program stored on the recording medium of the computer and executes the processes according to the read program. In another mode of execution of the program, the computer may read the program directly from a portable recording medium and execute the processes according to the program or may execute the processes according to the program each time the program is transferred from the server computer to the computer. Alternatively, the processes may be executed using a so-called ASP (Application Service Provider) service in which the program is not transferred from a server computer to the computer but process functions are implemented by instructions to execute the program and acquisition of the results of the execution. Note that the program in this mode encompasses information that is provided for processing by an electronic computer and is equivalent to the program (such as data that is not direct commands to a computer but has the nature that defines processing of the computer).

While the apparatuses are configured by causing a computer to execute a predetermined program in the embodiments described above, at least some of the processes may be implemented by hardware.

INDUSTRIAL APPLICABILITY

As has been described above, each of the computing apparatuses of the embodiments are capable of obtaining a correct result of computation by using the computation capability provided by the capability providing apparatus even if the capability providing apparatus is in a condition where the capability providing apparatus does not always perform a correct process. Accordingly, the computing apparatus does not need to perform verification for confirming the validity of the capability providing apparatus. Furthermore, if a plurality of computing apparatuses share the capability providing apparatus, the computing apparatuses can obtain a correct result of computation.

Such a proxy computing system can be used in, for example, volunteer-based distributed computing, P2P computing services, computing services paid for with payments for advertisements, computing services that are provided as network services or public infrastructures, and network services that are substitutes for computing packages licensed in the form of libraries.

DESCRIPTION OF REFERENCE NUMERALS

-   -   1-5, 101-105, 201-207: Proxy computing system     -   11-61, 111-151, 211-271: Computing apparatus     -   12-62, 112-152, 212-272: Capability providing apparatus. 

What is claimed is:
 1. A proxy computing system comprising a capability providing apparatus and a number Φ of computing apparatuses φ, wherein φ=1, . . . , Φ, Φ is an integer greater than or equal to 2, G_(φ) and H_(φ) are groups, f_(φ) is a function that maps an element of the group H_(φ) to an element of the group G_(φ), a(φ) and b(φ) are natural numbers that are relatively prime to each other, a class CL_(φ)(M_(φ)) corresponding to an element M_(φ) of the group H_(φ) is a set whose members are elements f_(φ)(M_(φ))^(a(φ)b(φ)) of the group G_(φ), X_(φ,1) and X_(φ,2) are random variables having values in the group G_(φ), x_(φ,1) is a realization of the random variable X_(φ,1), and x_(φ,2) is a realization of the random variable X_(φ,2); each of the computing apparatuses φ comprises processing circuitry configured to implement an input information providing unit outputting first input information τ_(φ,1) and second input information τ_(φ,2) that correspond to an element x_(φ) of the group H_(φ) and are elements of the group H_(φ); the capability providing apparatus comprises processing circuitry configured to implement: a first output information computing unit using the first input information τ_(φ,1) to correctly compute f_(φ)(τ_(φ,1)) with a probability greater than a certain probability and setting an obtained result of the computation as first output information z_(φ,1); and a second output information computing unit using the second input information τ_(φ,2) to correctly compute f_(φ)(τ_(φ,2)) with a probability greater than a certain probability and setting an obtained result of the computation as second output information z_(φ,2); and the processing circuitry of each of the computing apparatuses φ is further configured to implement: a first computing unit configured to generate a computation result u_(φ)=f_(φ)(x_(φ))^(b(φ))x_(φ,1) from the first output information z_(φ,1), and to generate and store a computation result u_(φ)′=u_(φ) ^(a(φ)); a determining unit configured to determine whether or not there is a pair of u_(φ)′ and v_(φ)′, from a previously stored pair (v_(φ), v_(φ)′), that are in a same class, and when there is not a pair of u_(φ)′ and v_(φ)′ that are in a same class, a second computing unit is configured to generate and store a computation result of new v_(φ)=f_(φ)(x_(φ))^(a(φ))x_(φ,2) and new v_(φ)′=v_(φ) ^(b(φ)) from the second output information z_(φ,2); a final output unit configured to output, when there is a pair of u_(φ)′ and v_(φ)′ determined to be in the same class, a value u_(φ) ^(b′)(φ)v_(φ) ^(a′(φ)) for the computation results u_(φ) and v_(φ) where a value u_(φ) ^(a(φ)) for the computation result u and a value v_(φ) ^(b(φ)) for the computation result v_(φ) belong to a class CL_(φ)(M_(φ)) corresponding to the same element M_(φ) and for integers a′(φ) and b′(φ) that satisfy a′(φ)a(φ)+b′(φ)b(φ)=1, wherein the determining unit determines that calculation of u_(φ) ^(b′(φ))v_(φ) ^(a′(φ)) is impossible based on a calculation reliability of the capability providing apparatus being lower than a reference value when there are no values of u_(φ)′ and v_(φ)′ that are determined to be in the same class after the predetermined number of iterations are completed, such that the capability providing apparatus provides a computing capability to each of the computing apparatuses, without leaking secret information, and each of the computing apparatuses is configured to utilize the commuting capability, via the capability providing apparatus, to obtain a computing result f_(φ)(x_(φ)) when the calculation reliability of the capability providing apparatus is verified to be above a predetermined level.
 2. The proxy computing system according to claim 1, wherein all of the groups G_(φ)(φ=1, . . . , Φ) are the same, all of the groups H_(φ)(φ=1, . . . , Φ) are the same, and all of the functions f_(φ)(φ=1, . . . , Φ) are the same; the first output information computing unit correctly computes f_(φ)(τ_(φ,1)) with a probability greater than a certain probability, the first output information computing unit computing f_(φ)(τ_(φ,1)) independently for each φ; and the second output information computing unit correctly computes f_(φ)(τ_(φ,2)) with a probability greater than a certain probability, the second output information computing unit computing f_(φ)(τ_(φ,2)) independently for each φ.
 3. The proxy computing system according to claim 2, wherein each of the computing apparatuses φ comprises a natural number selecting unit selecting at least one of the natural numbers a(φ) and b(φ); and the first input information τ_(φ,1) further corresponds to the natural number b(φ) and the second input information τ₂ further corresponds to the natural number a(φ).
 4. The proxy computing system according to claim 1, wherein each of the computing apparatuses φ comprises a natural number selecting unit selecting at least one of the natural numbers a(φ) and b(φ); and the first input information τ_(φ,1) further corresponds to the natural number b(φ) and the second input information τ_(φ,2) further corresponds to the natural number a(φ).
 5. The proxy computing system according to claim 1, wherein the input information providing unit sets pieces of information whose relation with the element x_(φ) is scrambled as the first input information τ_(φ,1) and the second input information τ_(φ,2).
 6. The proxy computing system according to claim 2, wherein the input information providing unit sets pieces of information whose relation with the element x_(φ) is scrambled as the first input information τ_(φ,1) and the second input information τ_(φ,2).
 7. The proxy computing system according to claim 3, wherein the input information providing unit sets pieces of information whose relation with the element x_(φ) is scrambled as the first input information τ_(φ,1) and the second input information τ_(φ2).
 8. The proxy computing system according to claim 4, wherein the input information providing unit sets pieces of information whose relation with the element x_(φ) is scrambled as the first input information τ_(φ,1) and the second input information τ_(φ,2).
 9. The proxy computing system according to claim 1, wherein the function f_(φ) is a homomorphic function.
 10. The proxy computing system according to claim 9, wherein the function f_(φ) is a homomorphic function, the group H_(φ) is a cyclic group, μ_(φ,h) is a generator of the cyclic group H_(φ), K_(φ,H) is the order of the cyclic group H_(φ), and v_(φ)=f_(φ)(μ_(φ,h)); the input information providing unit comprises: a first random number generator generating a random number r(φ, 1) that is a natural number greater than or equal to 0; a first input information computing unit computing μ_(φ,h) ^(r(φ,1))x_(φ) ^(b(φ)) as the first input information τ_(φ,1); a second random number generator generating a random number r(φ, 2) that is a natural number greater than or equal to 0; and a second input information computing unit computing μ_(φ,h) ^(r(φ,2))x_(φ) ^(a(φ)) as the second input information τ_(φ,2); the first output information computing unit uses the first input information μ_(φ,h) ^(r(φ,1))x_(φ) ^(b(φ)) to correctly compute f_(φ)(μ_(φ,h) ^(r(φ,1))x_(φ) ^(b(φ))) with a probability greater than a certain probability and sets an obtained result of the computation as the first output information z_(φ,1); the second output information computing unit uses the second input information μ_(φ,h) ^(r(φ,2))x_(φ) ^(a(φ)) to correctly compute f_(φ)(μ_(φ,h) ^(r(φ,2))x_(φ) ^(a(φ))) with a probability greater than a certain probability and sets an obtained result of the computation as the second output information Z_(φ,2); the first computing unit computes z_(φ,1)v_(φ) ^(−r(φ,1)) to obtain the computation result u_(φ); and the second computing unit computes z_(φ,2)v_(φ) ^(−r(φ,2)) obtain the computation result v_(φ).
 11. The proxy computing system according to claim 10, wherein the first random number generator generates the random number r(φ, 1) when b(φ)≠1; the first input information computing unit computes the μ_(φ,h) ^((φ,1))x_(φ) ^(b(φ)) as the first input information τ_(φ,1) when b(φ)≠1; the first output information computing unit sets, as the first output information z_(φ,1), the computation result obtained by using the first input information μ_(φ,h) ^(r(φ,1))x_(φ) ^(b(φ)) when b(φ)≠1; the first computing unit computes z_(φ,1)v_(φ) ^(−r(φ,1)) to obtain the computation result u_(φ) when b(φ)≠1; the second random number generator generates the random number r(φ, 2) when a(φ)≠1; the second input information computing unit computes μ_(φ,h) ^(r(φ,2))x_(φ) ^(a(φ)) as the second input information τ_(φ,2) when a(φ)≠1; the second output information computing unit sets, as the second output information z_(φ,2), the computation result obtained by using the second input information μ_(φ,h) ^(r(φ,2))x_(φ) ^(a(φ)) when a(φ)≠1; the second computing unit computes z_(φ,2)v_(φ) ^(−(φ,2)) to obtain the computation result v_(φ) when a(φ)≠1; the input information providing unit comprises: a third random number generator generating a random number r(φ, 3) that is a natural number greater than or equal to 0; and a third input information computing unit setting x_(φ) ^(r(φ,3)) as the first input information τ_(φ,1) when b(φ)=1 and sets x_(φ) ^(r(φ,3)) as the second input information τ_(φ,2) when a(φ)=1; the capability providing apparatus comprises a third output information computing unit using the x_(φ) ^(r(φ, 3)) to correctly compute f_(φ)(x_(φ) ^(r(φ,3)) with a probability greater than a certain probability and setting an obtained result of the computation as third output information z_(φ,3); and each of the computing apparatuses φ comprises a third computing unit setting z_(φ,3) ^(1/r(φ, 3)) as the computation result u_(φ) when b(φ)=1 and setting z_(φ,3) ^(1/r(φ, 3)) as the computation result v_(φ) when a(φ)=1.
 12. The proxy computing system according to claim 1, wherein the group H_(φ) is a direct product group G_(φ)×G_(φ), the function f_(φ) is a homomorphic function, the group G_(φ) is a cyclic group, a generator of the cyclic group G_(φ) is μ_(φ,g), the order of the cyclic group G_(φ) is K_(φ,G), x_(φ)=(c_(φ,1), c_(φ,2)), (V_(φ), W_(φ)) is an element of the group H_(φ), and f_(φ)(V_(φ), W_(φ))=Y_(φ); the input information providing unit comprises: a fourth random number generator generating a random number r(φ, 4) that is a natural number greater than or equal to 0; a fifth random number generator generating a random number r(φ, 5) that is a natural number greater than or equal to 0; a first input information computing unit computing c_(φ,2) ^(b(φ))W_(φ) ^(r(φ,4)) and c_(φ,1) ^(b(φ))V^(r(φ,4))μ_(φ,g) ^(r(φ,5)) as the first input information τ_(φ,1); a sixth random number generator generating a random number r(φ, 6) that is a natural number greater than or equal to 0; a seventh random number generator generating a random number r(φ, 7) that is a natural number greater than or equal to 0; and a second input information computing unit computing c_(φ,2) ^(a(φ))W_(φ) ^(r(φ,6)) and C_(φ,1) ^(a(φ))V^(r(φ,6))μ_(φ,g) ^(r(φ,7)) as the second input information τ_(φ,2); the first output information computing unit uses the first input information c_(φ,1) ^(b(φ))V_(φ) ^(r(φ,4))μ_(φ,g) ^(r(φ,5)) and c_(φ,2) ^(b(φ))W_(φ) ^(r(φ,4)) to correctly compute f_(φ)(c_(φ,1) ^(b(φ))V_(φ) ^(r(φ,4))μ_(φ,g) ^(r(φ,5)), c_(φ,2) ^(b(φ))W_(φ) ^(r(φ,4))) with a probability greater than a certain probability and sets an obtained result of the computation as the first output information z_(φ,1); the second output information computing unit uses the second input information c_(φ,1) ^(a(φ))V_(φ) ^(r(φ,6))μ_(φ,g) ^(r(φ,7)) and c_(φ,2) ^(a(φ))W_(φ) ^(r(φ,6)) to correctly compute f_(φ)(c_(φ,1) ^(a(φ))V_(φ) ^(r(φ,6))μ_(φ,g) ^(r(φ,7)), c_(φ,2) ^(a(φ))W_(φ) ^(r(φ,6))) with a probability greater than a certain probability and sets an obtained result of the computation as the second output information z_(φ,2); the first computing unit computes z_(φ,1)Y_(φ) ^(−r(φ,4))μ_(φ,g) ^(−r(φ,5)) to obtain the computation result u; and the second computing unit computes z_(φ,2)Y_(φ) ^(−r(φ,6))μ_(φ,g) ^(−r(φ,7)) to obtain the computation result v.
 13. The proxy computing system according to claim 1, wherein the function f_(φ)(x_(φ)) is a homomorphic function for converting the element x_(φ)=C_(φ,1)(y(φ,1), m_(φ)) which is an element of the group H_(φ) to an element f_(φ)(x_(φ))=C_(φ,2)(y(φ,2), m_(φ)) of the group G_(φ), φ=1, . . . , Φ, C_(φ,1)(y(φ,1), m_(φ)) is a ciphertext obtained by encrypting a plaintext m_(φ) with a first encryption key y(φ, 1) according to a first encryption scheme ENC_(φ,1), and C_(φ,2)(y(φ, 2), m_(φ)) is a ciphertext obtained by encrypting the plaintext m_(φ) with a second encryption key y(φ, 2) according to a second encryption scheme ENC_(φ,2); the input information providing unit comprises: a first random number generator generating an arbitrary element _(h)r_(φ,1) of the group H_(φ); a first input information computing unit computing x_(φ) ^(b(φ))C_(φ,1)(y(φ, 1), _(h)r_(φ,1)) as the first input information τ_(φ,1); a second random number generator generating an arbitrary element _(h)r_(φ,2) of the group H_(φ); and a second input information computing unit computing x_(φ) ^(a(φ))C_(φ,1)(y(φ, 1), _(h)r_(φ,2)) as the second input information τ_(φ,2); the first output information computing unit uses the first input information x_(φ) ^(b(φ))C_(φ,1)(y(φ, 1), _(h)r_(φ,1)) to correctly compute f_(φ)(x_(φ) ^(b(φ))C_(φ,1)(y(φ, 1), _(h)r_(φ,1))) with a probability greater than a certain probability and sets an obtained result of the computation as the first output information z_(φ,1); the second output information computing unit uses the second input information x_(φ) ^(b(φ))C_(φ,1)(y(φ, 1), _(h)r_(φ,2)) to correctly compute f_(φ)(x_(φ) ^(a(φ))C_(φ,1)(y(φ, 1), _(h)r_(φ,2))) with a probability greater than a certain probability and sets a result of the computation as the second output information Z_(φ,2); the first computing unit computes z_(φ,1)(C_(φ,2)(y(φ, 2), _(h)r_(φ,1)))⁻¹ to obtain the computation result u_(φ); and the second computing unit computes z_(φ,2)(C_(φ,2)(y(φ, 2), _(h)r_(φ,2)))⁻¹ to obtain the computation result v_(φ).
 14. The proxy computing system according to claim 13, wherein the group G_(φ) is a direct product group G_(φ,1)×G_(φ,2) of cyclic groups G_(φ,1) and G_(φ,2), μ_(φ,g1) is a generator of the cyclic group G_(φ,1), μ_(φ,g2) is a generator of the cyclic group G_(φ,2), s(φ, 2) is a decryption key corresponding to the second encryption key y(φ, 2), the second encryption key y(φ, 2) is μ_(φ,g2) ^(s(φ,2)), r(φ) is an integer random number, the element C_(φ,2)(y(φ, 2), m_(φ)) is (μ_(φ,g1) ^(r(φ)), m_(φ)y(φ, 2)^(φ))), the value u_(φ) ^(a(φ)) is (c_(φ,1u), c_(φ,2u))ϵG_(φ,1)×G_(φ,2), the value v_(φ) ^(b(φ)) is (c_(φ1v), c_(φ,2v))ϵG_(φ,1)×G_(φ,2), and e_(φ)(α, β) is a bilinear map that yields an element of a cyclic group G_(φ,T) for (α, β)ϵG_(φ,1)×G_(φ,2); and the final output unit outputs the value u_(φ) ^(b′(φ))v_(φ) ^(a′(φ)) that satisfies a relation e_(φ)(μ_(φ,g1), c_(φ,2u))/e_(φ)(c_(φ,1u), y(φ,2))=e_(φ)(μ_(φ,g1), c_(φ,g1), c_(φ,2v))/e_(φ)(c_(φ,1v), y(φ, 2)).
 15. The proxy computing system according to claim 14, wherein N_(φ) is the composite number of a prime ω_(φ) and a prime ι_(φ), each of the groups G_(φ,1) and G_(φ,2) is a subgroup consisting of points on a first elliptic curve E_(φ,1) defined on a factor ring modulo the composite number N_(φ), each of G_(φ,1ω) and G_(φ,2ω) is a subgroup consisting of points on a second elliptic curve E_(φ,2) defined on a factor ring modulo the prime ω_(φ), each of G_(φ,1ι) and G_(φ,2ι) is a subgroup consisting of points on a third elliptic curve E_(φ,3) defined on a factor ring modulo the prime ι_(φ), e_(φ,ω)(α_(ω), β_(ω)) is a second bilinear map that yields an element of a cyclic group G_(φ,Tω) for (α_(ω), β_(ω))ϵG_(φ,1ω)×G_(φ,2ω), e_(φ,ι)(α_(ι), β_(ι)) is a third bilinear map that yields an element of a cyclic group G_(φ,Tι) for (α_(ι), β_(ι))ϵG_(φ,1ι)×G_(ι,2ι), HM_(φ) is an isomorphism map that maps a point on the first elliptic curve E_(φ,1) to a point on the second elliptic curve E_(φ,2) and a point on the third elliptic curve E_(φ,3), and HM_(φ) ⁻¹ is the inverse map of the isomorphism map HM_(φ); and each of the computing apparatuses φ comprises: a determining unit using the isomorphic map HM_(φ) to map a point α on the first elliptic curve E_(φ,1) to a point θ_(ω)(α) on the second elliptic curve E_(φ,2) and a point θ_(ι)(α) on the third elliptic curve E_(φ,3), using the isomorphic map HM_(φ) to map a point β on the first elliptic curve E_(φ,1) to a point θ_(ω)(β) on the second elliptic curve E_(φ,2) and a point θ_(ι)(β) on the third elliptic curve E_(φ,3), obtaining e_(φ,ω)(θ_(ω)(α), θ_(ω)(β)) and e_(φ,ι)(θ_(ι)(α), θ_(ι)(β)), obtaining the inverse map H_(φ) ⁻¹ for e_(φ,ω)(θ_(ω)(α), θ_(ω)(β)) and e_(φ,ι)(θ_(ι)(α), θ_(ι)(β)) as e_(φ)(α, β), and determining whether or not the relation is satisfied.
 16. The proxy computing system according to claim 1, wherein the group H_(φ) is a direct product group H_(1,φ)×H_(2,φ) of cyclic groups H_(1,φ) and H_(2,φ) a generator of the cyclic group H_(1,φ) is η_(1,φ), a generator of the cyclic group H_(2,φ) is η_(2,φ), f_(φ) is a bilinear map that maps a pair of an element of the cyclic group H_(1,φ) and an element of the cyclic group H_(2,φ) to an element of the cyclic group G_(φ), an element x_(φ) of the group H_(φ) is a pair of an element λ_(1,φ) of the cyclic group H_(1,φ) and an element λ_(2,φ) of the cyclic group H_(2,φ) and Ω_(φ)=f_(φ)(η_(1,φ),η_(2,φ)); the input information providing unit comprises: a first random number generator generating random numbers r(φ, 11), r(p, 12), r(φ, 13), r(φ,14), r(φ, 15), and r(φ, 16) that are natural numbers greater than or equal to 0; a first input information computing unit computing (λ_(1,φ)η_(1,φ) ^(r(φ,11)·r(φ,12)), λ_(2,φ) ^(b(φ))η_(2,φ) ^(r(φ,13)·(φ,14))), (η_(1,φ) ^(r(φ,11)), λ_(2,φ) ^(−b(φ)·r(φ,12))η_(2,φ) ^(r(φ,15))) and (λ_(1,φ) ^(−(φ,14))η_(1,φ) ^(−r(φ,16)), η_(2,φ) ^(r(φ,13))) as the first input information τ_(φ,1); a second random number generator generating random numbers r(φ, 21), r(φ, 22), r(φ, 23), r(φ,24), r(φ, 25), and r(φ, 26) that are natural numbers greater than or equal to 0; and a second input information computing unit computing (λ_(1,φ)η_(1,φ) ^(r(φ,21)·r(φ,22)), λ_(2,φ) ^(a(φ))η_(2,φ) ^(r(φ,23)·r(φ,24))), (η_(1,φ) ^(r(φ,21)), λ_(2,φ) ^(−a(φ)·r(φ,22))η_(2,φ) ^(r(φ,25))) and (λ_(1,φ) ^(−r(φ,24))η_(1,φ) ^(r(φ,26)), η_(2,φ) ^(r(φ,23))) as second input information τ_(φ,2); the first output information computing unit uses the first input information τ_(φ,1) to correctly compute correctly compute f_(φ)(λ_(1,φ)η_(1,φ) ^(r(φ,11)·r(φ,12)), λ_(2,φ) ^(b(φ))η_(2,φ) ^(r(φ,13)·(φ,14))), f_(φ)(η_(1,φ) ^(r(φ,11)), λ_(2,φ) ^(−b(φ)·r(φ,12))η_(2,φ) ^(r(φ,15))), and f_(φ)(λ_(1,φ) ^(−r(φ,14))η_(1,φ) ^(r(φ,16)), η_(2,φ) ^(r(φ,13))) with a probability greater than a certain probability and sets the obtained results of the computations z_(φ,1,1), z_(φ,1,2), z_(φ,1,3) as first output information z_(φ,1); the second output information computing unit uses the second input information τ_(φ,2) to correctly compute f_(φ)(λ_(1,φ)η_(1,φ) ^(r(φ,21)·r(φ,22)), λ_(2,φ) ^(a(φ))η_(2,φ) ^(r(φ,23)·(φ,24))), f_(φ)(η_(1,φ) ^(r(φ,21)), λ_(2,φ) ^(−a(φ)·r(φ,22))η_(2,φ) ^(r(φ,25))), and f_(φ)(λ_(1,φ) ^(−r(φ,24))η_(1,φ) ^(r(φ,26)), η_(2,φ) ^(r(φ,23))) with a probability greater than a certain probability and sets the obtained results of the computations, z_(φ,2,1), z_(φ,2,2) and z_(φ,2,3), as second output information z_(φ,2); the first computing unit computes z_(φ,1,1)z_(φ,1,2)z_(1,3)Ω_(φ) ^(−r(φ,11)·r(φ,12)·r(φ,13)·r(φ,14)−r(φ,11)·r(φ,15)−r(φ,13)·r(φ,16)) to obtain the computation result u_(φ); the second computing unit computes z_(φ,2,1)z_(φ,2,2)z_(2,3)Ω_(φ) ^(−r(φ,21)·r(φ,22)·r(φ,23)·r(φ,24)−r(φ,21)·r(φ,25)−r(φ,23)·r(φ,26)) to obtain the computation result v_(φ); and the final output unit outputs a value u_(φ) ^(b′(φ))v_(φ) ^(a′(φ)) for integers a′(φ) and b′(φ) that satisfy a′(φ)a(φ)+b′(p)b(φ)=1 when the computation results u_(φ) and v_(φ) satisfy u_(φ) ^(a(φ))=v_(φ) ^(b(φ)).
 17. The proxy computing system according to claim 1, wherein a probability distribution of an error of the computation result u_(φ) for f_(φ)(x_(φ))^(b(φ)) does not depend on the natural number b(φ) and/or a probability distribution of an error of the computation result v_(φ) for f_(φ)(x_(φ))^(a(φ)) does not depend on the natural number a(φ), or there is a probability distribution that is indistinguishable from a probability distribution of an error of the computation result u_(φ) for f_(φ)(x_(φ))^(b(φ)) and does not depend on the natural number b(φ) and/or there is a probability distribution that is indistinguishable from a probability distribution of an error of the computation result v_(φ) for f_(φ)(x_(φ))^(a(φ)) and does not depend on the natural number a(φ).
 18. The proxy computing system according to claim 1, wherein the natural number a(φ) or the natural number b(φ) is a constant.
 19. A capability providing apparatus comprising: processing circuitry configured to implement a first output information computing unit and a second output information computing unit, wherein φ=1, . . . , Φ, Φ is an integer greater than or equal to 2, G_(φ) and H_(φ) are groups, f_(φ) is a function that maps an element of the group H_(φ) to an element of the group G_(φ), a(φ) and b(φ) are natural numbers that are relatively prime to each other, a class CL_(φ)(M_(φ)) corresponding to an element M_(φ) of the group H_(φ) is a set whose members are elements f_(φ)(M_(φ))^(a(φ)b(φ)) of the group G_(φ), X_(φ,1) and X_(φ,2) are random variables having values in the group G_(φ), x_(φ,1) is a realization of the random variable X_(φ,1), and x_(φ,2) is a realization of the random variable X_(φ,2); wherein the processing circuitry receives, from a computing apparatus, first input information τ_(φ,1) and second input information τ_(φ,2) that correspond to an element x_(φ) of the group H_(φ) and are elements of the group H_(φ); the first output information computing unit being configured to use the first input information τ_(φ,1) to correctly compute f_(φ)(τ_(φ,1)) with a probability greater than a certain probability and set an obtained result of the computation as first output information z_(φ,1); and the second output information computing unit being configured to use the second input information τ_(φ,2) to correctly compute f_(φ)(τ_(φ,2)) with a probability greater than a certain probability and set an obtained result of the computation as second output information z_(φ,2), wherein the processing circuitry transmits the first output information z_(φ,1) and the second output information z_(φ,2) to the computing apparatus, and the computing apparatus includes processing circuitry further configured to implement: a first computing unit configured to generate a computation result u_(φ)=f_(φ)(x_(φ))^(b(φ))x_(φ,1) from the first output information z_(φ,1), and to generate and store a computation result u_(φ)′=u_(φ) ^(a(φ)); a determining unit configured to determine whether or not there is a pair of u_(φ)′ and v_(φ)′ from a previously stored pair (v_(φ),v_(φ)′), that are in a same class, and when there is not a pair of u_(φ)′ and v_(φ)′ that are in a same class, a second computing unit is configured to generate and store a computation result of new v_(φ)=f_(φ)(x_(φ))x^(a(φ))x_(φ,2) and new v_(φ)′=v_(φ) ^(b(φ)) from the second output information Z_(φ,2); a final output unit configured to output, when there is a pair of u_(φ)′ and v_(φ)′ determined to be in the same class, a value u_(φ) ^(b′(φ))v_(φ) ^(a′(φ)) for the computation results u_(φ) and v_(φ) where a value u_(φ) ^(a(φ)) for the computation result u_(φ) and a value v_(φ) ^(b(φ)) for the computation result v_(φ) belong to a class CL_(φ)(M_(φ)) corresponding to the same element M_(φ) and for integers a′(φ) and b′(φ) that satisfy a′(φ)a(φ)+b′(φ)b(φ)=1, wherein the determining unit determines that calculation of u_(φ) ^(b′(φ))v_(φ) ^(a′(φ)) is impossible based on a calculation reliability of the capability providing apparatus being lower than a reference value when there are no values of u_(φ)′ and v_(φ)′ that are determined to be in the same class after the predetermined number of iterations are completed, such that the capability providing apparatus provides a computing capability to the computing apparatuses, without leaking secret information, and the computing apparatus is configured to utilize the computing capability, via the capability providing apparatus, to obtain a computing result f_(φ)(x_(φ)) when the calculation reliability of the capability providing apparatus is verified to be above a predetermined level.
 20. The capability providing apparatus according to claim 19, wherein all of the groups G_(φ) (φ=1, . . . , Φ) are the same, all of the groups H_(φ)(φ=1, . . . , Φ) are the same, and all of the functions f₁₀₀ (φ=1, . . . , Φ) are the same; the first output information computing unit correctly computes f_(φ)(τ_(φ,1)) with a probability greater than a certain probability, the first output information computing unit computing f_(φ)(τ_(φ,1)) independently for each φ; and the second output information computing unit correctly computes f_(φ)(τ_(φ,2)) with a probability greater than a certain probability, the second output information computing unit computing f_(φ)(τ_(φ,2)) independently for each φ.
 21. The capability providing apparatus according to claim 19, wherein the function f_(φ) is a homomorphic function, the group H_(φ) is a cyclic group, μ_(φ,h) is a generator of the cyclic group H_(φ), K_(φ,h) is the order of the cyclic group H_(φ), and v_(φ)=f_(φ)(μ_(φ,h)); the first output information computing unit uses the first input information μ_(φ,h) ^(r(φ,1))x_(φ) ^(b(φ)) to correctly compute f_(φ)(μ_(φ,h) ^(r(φ,1))x_(φ) ^(b(φ))) with a probability greater than a certain probability and sets an obtained result of the computation as the first output information z_(φ,1); and the second output information computing unit uses the second input information μ_(φ,h) ^(r(φ,2))x_(φ) ^(a(φ)) to correctly compute f_(φ)(μ_(φ,h) ^(r(φ,2))x_(φ) ^(a(φ))) with a probability greater than a certain probability and sets an obtained result of the computation as the second output information z_(φ,2).
 22. The capability providing apparatus according to claim 19, wherein the group H_(φ) is a direct product group G_(φ)×G_(φ), the decryption function f_(φ) is a homomorphic function, the group G_(φ) is a cyclic group, a generator of the cyclic group G_(φ) is μ_(φ,g), the order of the cyclic group G_(φ) is K_(φ,G), x_(φ)=(c_(φ,1), c_(φ,2)), (V_(φ), W_(φ)) is an element of the group H_(φ), f_(φ)(V_(φ), W_(φ))=Y_(φ), and r(φ,4), r(φ, 5), r(φ, 6) and r(φ,7) are random numbers that are natural numbers greater than or equal to 0; the first output information computing unit uses the first input information input information c_(φ,1) ^(b(φ))V_(φ) ^(r(φ,4))μ_(φ,g) ^(r(φ,5)) and c_(φ,2) ^(b(φ))W_(φ) ^(r(φ,4)) to correctly compute f_(φ)(c_(φ,1) ^(b(φ))V_(φ) ^(r(φ,4))μ_(φ,g) ^(r(φ,5)), c_(φ,2) ^(b(φ))W_(φ) ^(r(φ,4))) with a probability greater than a certain probability and sets an obtained result of the computation as the first output information z_(φ,1); and the second output information computing unit uses the second input information c_(φ,1) ^(a(φ))V_(φ) ^(r(φ,6))μ_(φ,g) ^(r(φ,7)) and c_(φ,2) ^(a(φ))W_(φ) ^(r(φ,6)) to correctly compute f_(φ)(c_(φ,1) ^(a(φ))V_(φ) ^(r(φ,6))μ_(φ,g) ^(r(φ,7)), c_(φ,2) ^(a(φ))W_(φ) ^(r(φ,6))) with a probability greater than a certain probability and sets an obtained result of the computation as the second output information z_(φ,2).
 23. The capability providing apparatus according to claim 19, wherein the function f_(φ)(x_(φ)) is a homomorphic function for converting the element x_(φ)=C_(φ,1)(y(φ,1), m_(φ)) which is an element of the group H_(φ) to an element f_(φ)(x_(φ))=C_(φ,2)(y(φ,2), m_(φ)) of the group G_(φ), φ=1, . . . , Φ, C_(φ,1)(y(φ,1), m_(φ)) is a ciphertext obtained by encrypting a plaintext m_(φ) with a first encryption key y(φ, 1) according to a first encryption scheme ENC_(φ,1), and C_(φ,2)(y(φ, 2), m_(φ)) is a ciphertext obtained by encrypting the plaintext m_(φ) with a second encryption key y(φ, 2) according to a second encryption scheme ENC_(φ,2), and _(h)r_(φ,1) and _(h)r_(φ,2) are arbitrary elements of the group H_(φ); the first output information computing unit uses the first input information x_(φ) ^(b(φ))C_(φ,1)(y(φ, 1), _(h)r_(φ,1)) to correctly compute f_(φ)(x_(φ) ^(b(φ))C_(φ,1)(y(φ, 1), _(h)r_(φ,1))) with a probability greater than a certain probability and sets an obtained result of the computation as the first output information z_(φ,1); and the second output information computing unit uses the second input information x_(φ) ^(a(φ))C_(φ,1)(y(φ, 1), _(h)r_(φ,2)) to correctly compute f_(φ)(x_(φ) ^(a(φ))C_(φ,1)(y(φ, 1), _(h)r_(φ,2))) with a probability greater than a certain probability and sets a result of the computation as the second output information Z_(φ,2).
 24. The capability providing apparatus according to claim 19, wherein the group H_(φ) is a direct product group H_(1,φ)×H_(2,φ) of cyclic groups H_(1,φ) and H_(2,φ), a generator of the cyclic group H_(1,φ) is η_(1,φ), a generator of the cyclic group H_(2,φ) is η_(2,φ), f_(φ) is a bilinear map that maps a pair of an element of the cyclic group H_(1,φ) and an element of the cyclic group H_(2,φ) to an element of the cyclic group G_(φ), and an element x_(φ) of the group H_(φ) is a pair of an element λ_(1,φ) of the cyclic group H_(1,φ) and an element λ_(2,φ) of the cyclic group H_(2,φ), Ω_(φ)=f_(φ)(η_(1,φ), η_(2,φ)), r(φ,11), r(φ, 12), r(φ, 13), (φ, 14), r(φ, 15), r(φ, 16), (φ, 21), r(φ, 22), r(φ, 23), r(φ, 24), (φ,25), and r(φ, 26) are random numbers that are natural numbers greater than or equal to 0, the first input information τ_(φ,1) is λ_(1,φ)η_(1,φ) ^(r(φ,11)·r(φ,12)), λ_(2,φ) ^(b(φ))η_(2,φ) ^(r(φ,13)·(φ,14))), (η_(1,φ) ^(r(φ,11)), λ_(2,φ) ^(−b(φ)·r(φ,12))η_(2,φ) ^(r(φ,15))), and (λ_(1,φ) ^(−r(φ,14))η_(1,φ) ^(r(φ,16)), η_(2,φ) ^(r(φ,13))), and the second input information τ_(φ,2) is (λ_(1,φ)η_(1,φ) ^(r(φ,21)·(φ,22)), λ_(2,φ) ^(a(φ))η_(2,φ) ^(r(φ,23)·r(φ,24))), (η_(1,φ) ^(r(φ,21)), λ_(2,φ) ^(−a(φ)·r(φ,22))η_(2,φ) ^(r(φ,25))) and (λ_(1,φ) ^(−r(φ,24))η_(1,φ) ^(r(φ,26)), η_(2,φ) ^(r(φ,23))); the first output information computing unit uses the first input information τ_(φ,1) to correctly compute f_(φ)(λ_(1,φ)η_(1,φ) ^(r(φ,11)·r(φ,12)), λ_(2,φ) ^(b(φ))η_(2,φ) ^(r(φ,13)·(φ,14))), f_(φ)(η_(1,φ) ^(r(φ,11)), λ_(2,φ) ^(−b(φ)·r(φ,12))η_(2,φ) ^(r(φ,15))), and f_(φ)(λ_(1,φ) ^(−r(φ,14))η_(1,φ) ^(r(φ,16)), η_(2,φ) ^(r(φ,13))) with a probability greater than a certain probability and sets the obtained results of the computations z_(φ,1,1), z_(φ,1,2), z_(φ,1,3) as first output information z_(φ,1); and the second output information computing unit uses the second input information τ_(φ,2) to correctly compute f_(φ)(λ_(1,φ)η_(1,φ) ^(r(φ,21)·r(φ,22)), λ_(2,φ) ^(a(φ))η_(2,φ) ^(r(φ,23)·(φ,24))), f_(φ)(η_(1,φ) ^(r(φ,21)), λ_(2,φ) ^(−a(φ)·r(φ,22))η_(2,φ) ^(r(φ,25))), and f_(φ)(λ_(1,φ) ^(−r(φ,24))η_(1,φ) ^(r(φ,26)), η_(2,φ) ^(r(φ,23))) with a probability greater than a certain probability and sets the obtained results of the computations, z_(φ,2,1), z_(φ,2,2) and z_(φ,2,3), as second output information z_(φ,2).
 25. A proxy computing method comprising: an input information providing step outputting, on each of a number Φ of computing apparatuses φ, first input information τ_(φ,1) and second input information τ_(φ,2) that correspond to an element x_(φ) of the group H_(φ) and are elements of the group H_(φ); a first output information computing step using, on a capability providing apparatus, the first input information τ_(φ,1) to correctly compute f_(φ)(τ_(φ,1)) with a probability greater than a certain probability and setting an obtained result of the computation as first output information z_(φ,1); a second output information computing step of using, on the capability providing apparatus, the second input information τ_(φ,2) to correctly compute f_(φ)(τ_(φ,2)) with a probability greater than a certain probability and setting an obtained result of the computation as second output information z_(φ,2); a first computing step of generating, on each of the computing apparatuses φ, a computation result u_(φ)=f_(φ)(x_(φ))^(b(φ))x_(φ,1) from the first output information z_(φ,1), and generating and storing a computation result u_(φ)′=u_(φ) ^(a(φ)); a determining step of determining whether or not there is a pair of u_(φ)′ and v_(φ)′ from a previously stored pair (v_(φ), v_(φ)′), that are in a same class, and when there is not a pair of u_(φ)′ and v_(φ)′ that are in a same class, performing a second computing step of generating and storing a computation result of new v_(φ)=f_(φ)(x_(φ))^(a(φ))x_(φ,2) and new v_(φ)′=v_(φ) ^(b(φ)) from the second output information z_(φ,2); and a final output step of outputting, when there is a pair of u_(φ)′ and v_(φ)′ determined to be in the same class, on each of the computing apparatuses φ, a value u_(φ) ^(b′(φ))v_(φ) ^(a′(φ)) for the computation results u_(φ) and v_(φ) when a value u_(φ) ^(a(φ)) for the computation result u_(φ) and a value v_(φ) ^(b(φ)) for the computation result v_(φ) belong to a class CL_(φ)(M_(φ)) corresponding to the same element M_(φ) and for integers a′(φ) and b′(φ) that satisfy a′(φ)a(φ)+b′(φ)b(φ)=1, wherein the method includes determining that calculation of u_(φ) ^(b′(φ))v_(φ) ^(a′(φ)) is impossible based on a calculation reliability of the capability providing apparatus being lower than a reference value when there are no values of u_(φ)′ and v_(φ)′ that are determined to be in the same class after the predetermined number of iterations are completed, such that the capability providing apparatus provides a computing capability to each of the computing apparatuses, without leaking secret information, and each of the computing apparatuses is configured to utilize the computing capability, via the capability providing apparatus, to obtain a computing result f_(φ)(x_(φ)) when the calculation reliability of the capability providing apparatus is verified to be above a predetermined level, and wherein φ=1, . . . , Φ, Φ is an integer greater than or equal to 2, G_(φ) is a group, f_(φ) is a function that maps an element of the group H_(φ) to an element of the group G_(φ), a(φ) and b(φ) are natural numbers that are relatively prime to each other, the class CL_(φ)(M_(φ)) corresponding to an element M_(φ) of the group H_(φ) is a set whose members are elements f_(φ)(M_(φ))^(a(φ)b(φ)) of the group G_(φ), X_(φ,1) and X_(φ,2) are random variables having values in the group G_(φ), x_(φ,1) is a realization of the random variable X_(φ,1), and x_(φ,2) is a realization of the random variable X_(φ,2).
 26. The proxy computing method according to claim 25, wherein all of the groups G_(φ)(φ=1, . . . , Φ) are the same, all of the groups H_(φ)(φ=1, . . . , Φ) are the same, and all of the functions f_(φ), (φ=1, . . . , Φ) are the same; the first output information computing step correctly computes f_(φ)(τ_(φ,1)) with a probability greater than a certain probability, the first output information computing unit computing f_(φ)(τ_(φ,1)) independently for each φ; and the second output information computing step correctly computes f_(φ)(τ_(φ,2)) with a probability greater than a certain probability, the second output information computing unit computing f_(φ)(τ_(φ,2)) independently for each φ.
 27. A capability providing method implemented by a capability providing apparatus, wherein φ=1, . . . , Φ, Φ is an integer greater than or equal to 2, G_(φ) is a group, f_(φ) is a function that maps an element of the group H_(φ) to an element of the group G_(φ), a(φ) and b(φ) are natural numbers that are relatively prime to each other, the class CL_(φ)(M_(φ)) corresponding to an element M_(φ) of the group H_(φ) is a set whose members are elements f_(φ)(M_(φ))^(a(φ)b(φ)) of the group G_(φ), X_(φ,1) and X_(φ,2) are random variables having values in the group G_(φ), x_(φ,1) is a realization of the random variable X_(φ,1), and x_(φ,2) is a realization of the random variable X_(φ,2), the method comprising the steps of: receiving, from a computing apparatus, first input information τ_(φ,1) and second input information τ_(φ,2) that correspond to an element x_(φ) of the group H_(φ) and are elements of the group H_(φ); by a first output information computing unit, using first input information τ_(φ,1) to correctly compute f_(φ)(τ_(φ,1)) with a probability greater than a certain probability and setting an obtained result of the computation as first output information z_(φ,1); by a second output information computing unit, using the second input information τ_(φ,2) to correctly compute f_(φ)(τ_(φ,2)) with a probability greater than a certain probability and setting an obtained result of the computation as second output information z_(φ,2); transmitting the first output information z_(φ,1) and the second output information z_(φ,2) to the computing apparatus, wherein the computing apparatus includes processing circuitry further configured to implement: a first computing unit configured to generate a computation result u_(φ)=f_(φ)(x_(φ))^(b(φ))x_(φ,1) from the first output information z_(φ,1), and to generate and store a computation result u_(φ)′=u_(φ) ^(a(φ)); a determining unit configured to determine whether or not there is a pair of u_(φ)′ and v_(φ)′ from a previously stored pair (v_(φ), v_(φ)′), that are in a same class, and when there is not a air of u_(φ)′ and v_(φ)′ that are in a same class, a second computing unit is configured to generate and store a computation result of new v_(φ)=f_(φ)(x_(φ))^(a(φ))x_(φ,2) and new v_(φ)′=v_(φ) ^(b(φ)) from the second output information z_(φ,2); a final output unit configured to output, when there is a pair of u_(φ)′ and v_(φ)′ determined to be in the same class, a value u_(φ) ^(b′(φ))v_(φ) ^(a′(φ)) for the computation results u_(φ) and v_(φ) where a value u_(φ) ^(a(φ)) for the computation result u_(φ) and a value v_(φ) ^(b(φ)) for the computation result v_(φ) belong to a class CL_(φ)(M_(φ)) corresponding to the same element M_(φ) and for integers a′(φ) and b′(φ) that satisfy a′(φ)a(φ)+b′(φ)b(φ)=1, wherein the determining unit determines that calculation of u_(φ) ^(b′(φ))v_(φ) ^(a′(φ)) is impossible based on a calculation reliability of the capability providing apparatus being lower than a reference value when there are no values of u_(φ)′ and v_(φ)′ that are determined to be in the same class after the predetermined number of iterations are completed, such that the capability providing apparatus provides a computing capability to the computing apparatus, without leaking secret information, and the computing apparatus is configured to utilize the computing capability, via the capability providing apparatus, to obtain a computing result f_(φ)(x_(φ)) when the calculation reliability of the capability providing apparatus is verified to be above a predetermined level.
 28. The capability providing method according to claim 27, wherein all of the groups G_(φ)(φ=1, . . . , Φ) are the same, all of the groups H_(φ)(φ=1, . . . , Φ) are the same, and all of the functions f₁₀₀ (φ=1, . . . , Φ) are the same; the first output information computing step correctly computes f_(φ)(τ_(φ,1)) with a probability greater than a certain probability, the first output information computing unit computing f_(φ)(τ_(φ,1)) independently for each φ; and the second output information computing step correctly computes f_(φ)(τ_(φ,2)) with a probability greater than a certain probability, the second output information computing unit computing f_(φ)(τ_(φ,2)) independently for each φ.
 29. A non-transitory computer-readable recording medium storing a program for causing a computer to perform a proxy computing method comprising: a receiving step of receiving, on a capability providing apparatus, from a computing apparatus, first input information τ_(φ,1) and second input information τ_(φ,2) that correspond to an element x_(φ) of the group H_(φ) and are elements of the group H_(φ); a first output information computing step of using, on the capability providing apparatus, the first input information τ_(φ,1) to correctly compute f_(φ)(τ_(φ,1)) with a probability greater than a certain probability and setting an obtained result of the computation as first output information z_(φ,1); a second output information computing step of using, on the capability providing apparatus, the second input information τ_(φ,2) to correctly compute f_(φ)(τ_(φ,2)) with a probability greater than a certain probability and setting an obtained result of the computation as second output information z_(φ,2); a transmitting step of transmitting, on the capability providing apparatus, the first output information z_(φ,1) and the second output information z_(φ,2) to the computing apparatus, and the computing apparatus includes processing circuitry further configured to implement: a first computing unit configured to generate a computation result u_(φ)=f_(φ)(x_(φ))^(b(φ))x_(φ,1) from the first output information z_(φ,1), and to generate and store a computation result u_(φ)′=u_(φ) ^(a(φ)); a determining unit configured to determine whether or not there is a pair of u_(φ)′ and v_(φ)′ from a previously stored pair (v_(φ), v_(φ)′), that are in a same class, and when there is not a pair of u_(φ)′ and v_(φ)′ that are in a same class, a second computing unit is configured to generate and store a computation result of new v_(φ)=f_(φ)(x_(φ))^(a(φ))x_(φ,2) and new v_(φ)′=v_(φ) ^(b(φ)) from the second output information z_(φ,2); a final output unit configured to output, when there is a pair of u_(φ)′ and v_(φ)′ determined to be in the same class, a value u_(φ) ^(b′(φ))v_(φ) ^(a′(φ)) for the computation results u_(φ) and v_(φ) where a value u_(φ) ^(a(φ)) for the computation result u_(φ) and a value v_(φ) ^(b(φ)) for the computation result v_(φ) belong to a class CL_(φ)(M_(φ)) corresponding to the same element M_(φ) and for integers a′(φ) and b′(φ) that satisfy a′(φ)a(φ)+b′(φ)b(φ)=1, wherein the determining unit determines that calculation of u_(φ) ^(b′(φ))v_(φ) ^(a′(φ)) is impossible based on a calculation reliability of the capability providing apparatus being lower than a reference value when there are no values of u_(φ)′ and v_(φ)′ that are determined to be in the same class after the predetermined number of iterations are completed, such that the capability providing apparatus provides a computing capability to the computing apparatus, without leaking secret information, and the computing apparatus is configured to utilize the computing capability, via the capability providing apparatus, to obtain a computing result f_(φ)(x_(φ)) when the calculation reliability of the capability providing apparatus is verified to be above a predetermined level. 